Author name cluster

Imran Razzak

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33 papers

2 author rows

JBHI Journal 2026 Journal Article

Advancing Metaverse-Based Healthcare With Multimodal Neuroimaging Fusion via Multi-Task Adversarial Variational Autoencoder for Brain Age Estimation

Muhammad Usman
Azka Rehman
Abdullah Shahid
Abd Ur Rehman
Sung-Min Gho
Aleum Lee
Tariq M. Khan
Imran Razzak

The metaverse, which integrates physical and virtual realities through technologies such as high-speed internet, virtual and augmented reality, and artificial intelligence (AI), offers transformative prospects across various fields, particularly healthcare. This integration introduces a new paradigm in AI-driven medical imaging, particularly in assessing brain age–a crucial marker for detecting age-related neuropathologies such as Alzheimer's disease (AD) using magnetic resonance imaging (MRI). Despite advances in deep learning for estimating brain age from structural MRI (sMRI), incorporating functional MRI (fMRI) data presents significant challenges due to its complex data structure and the noisy nature of functional connectivity measurements. To address these challenges, we present the Multitask Adversarial Variational Autoencoder (M-AVAE), a bespoke deep learning framework designed to enhance brain age predictions through multimodal MRI data integration. The M-AVAE uniquely separates latent variables into generic and unique codes, effectively isolating shared and modality-specific features. Additionally, integrating multitask learning with sex classification as a supplementary task enables the model to account for sex-specific aging nuances. Evaluated on the OpenBHB dataset–a comprehensive multisite brain MRI aggregation–the M-AVAE demonstrates exceptional performance, achieving a mean absolute error of 2. 77 years, surpassing conventional methodologies. This success positions M-AVAE as a powerful tool for metaverse-based healthcare applications in brain age estimation.

AAAI Conference 2026 Conference Paper

Data-Centric Sequential Recommendation with Relation-Augmented Generation

Yichen Li
Yichen Tan
Yijing Shan
Haozhao Wang
Rui Zhang
Imran Razzak
Ruixuan Li

Data-Centric Sequential Recommendation (DaCSR) has emerged as a promising technique that enhances dataset quality to better capture user preferences without increasing training complexity. However, mining item relations to improve data quality remains challenging due to the intricate nature of interaction sequences. Existing methods predominantly either: 1) optimize models to learn such item relations from fixed datasets at significant training cost, or 2) employ generative models to adaptively learn only interaction patterns, which lack interpretability and cannot guarantee effective data quality enhancement. In this paper, we pioneer a relation-guided dataset augmentation and regeneration framework for sequential recommendation called \textbf{RaSR}. This framework can significantly improve model performance on original datasets while maintaining training efficiency without modifying the model architecture. Specifically, we first preprocess user interactions to construct standardized sequential data and extract semantic representations via a Large Language Model (LLM). We then build a multi-relation graph with manually predefined metrics and semantic representations to generate augmented datasets. Finally, a relation-aware generator can produce regenerated datasets with both the multi-relation graph and the augmented dataset. To verify the effectiveness of RaSR, we conduct experiments on various backbone models and datasets, and achieve significant performance improvement compared to training the model only on the original dataset.

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AAAI Conference 2026 Conference Paper

DeLo: Dual Decomposed Low-Rank Experts Collaboration for Continual Missing Modality Learning

Xiwei Liu
Yulong Li
Feilong Tang
Imran Razzak

Adapting Large Multimodal Models (LMMs) to real-world scenarios poses the dual challenges of learning from sequential data streams while handling frequent modality incompleteness, a task known as Continual Missing Modality Learning (CMML). However, existing works on CMML have predominantly relied on prompt tuning, a technique that struggles with this task due to cross-task interference between its learnable prompts in their shared embedding space. A naive application of Low-Rank Adaptation (LoRA) with modality-shared module will also suffer modality interference from competing gradients. To this end, we propose DeLo, the first framework to leverage a novel dual-decomposed low-rank expert architecture for CMML. Specifically, this architecture resolves modality interference through decomposed LoRA expert, dynamically composing LoRA update matrix with rank-one factors from disentangled modality-specific factor pools. Embedded within a task-partitioned framework that structurally prevents catastrophic forgetting, this expert system is supported by two key mechanisms: a Cross-Modal Guided Routing strategy to handle incomplete data and a Task-Key Memory for efficient, task-agnostic inference. Extensive experiments on established CMML benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches. This highlights the value of a principled, architecturally-aware LoRA design for real-world multimodal challenges.

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AAAI Conference 2026 Conference Paper

FedCD: Towards Consolidated Distillation for Heterogeneous Federated Learning

Yichen Li
Hang Su
Huifa Li
Haolin Yang
Xinlin Zhuang
Haochen Xue
Haozhao Wang
Imran Razzak

Knowledge Distillation (KD) serves as an effective approach to addressing heterogeneity issues in Federated Learning (FL), leveraging additional datasets to align local and global models better. There are two primary distillation paradigms: feature-based distillation, which utilizes intermediate-layer features of the network, and logit-based distillation, which employs the final layer's logit outputs. However, existing studies often select distillation methods based on intuitive and empirical evidence when facing different heterogeneous settings, neglecting the intrinsic relationship between distillation paradigms and heterogeneity. This oversight may result in suboptimal federated knowledge distillation performance under heterogeneous conditions. In this paper, we propose the Consolidated Distillation for Heterogeneous Federated Learning - FedCD that balances knowledge representations from both feature-based and logit-based distillation to enhance performance. Specifically, to address the misalignment between knowledge conveyed by features and logits, we aggregate features from different layers via cross-layer attention to preserve semantic knowledge, followed by distribution modeling using Gaussian Mixture Models. This process strengthens knowledge distillation by constraining the transformation of different network layers' features under a consolidated distribution, thereby mitigating impacts from both data and model heterogeneity. Extensive experiments demonstrate that FedCD outperforms state-of-the-art methods by over 10.72% and validate the effectiveness of our approach.

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JBHI Journal 2026 Journal Article

Two-Stage Self-Supervised Contrastive Learning Aided Transformer for Real-Time Medical Image Segmentation

Abdul Qayyum
Imran Razzak
Moona Mazher
Tariq Khan
Weiping Ding
Steven Niederer

The availability of large, high-quality annotated datasets in the medical domain poses a substantial challenge in segmentation tasks. To mitigate the reliance on annotated training data, self-supervised pre-training strategies have emerged, particularly employing contrastive learning methods on dense pixel-level representations. In this work, we proposed to capitalize on intrinsic anatomical similarities within medical image data and develop a semantic segmentation framework through a self-supervised fusion network, where the availability of annotated volumes is limited. In a unified training phase, we combine segmentation loss with contrastive loss, enhancing the distinction between significant anatomical regions that adhere to the available annotations. To further improve the segmentation performance, we introduce an efficient parallel transformer module that leverages Multiview multiscale feature fusion and depth-wise features. The proposed transformer architecture, based on multiple encoders, is trained in a self-supervised manner using contrastive loss. Initially, the transformer is trained using an unlabeled dataset. We then fine-tune one encoder using data from the first stage and another encoder using a small set of annotated segmentation masks. These encoder features are subsequently concatenated for the purpose of brain tumor segmentation. The multiencoder-based transformer model yields significantly better outcomes across three medical image segmentation tasks. We validated our proposed solution by fusing images across diverse medical image segmentation challenge datasets, demonstrating its efficacy by outperforming state-of-the-art methodologies.

AAAI Conference 2026 Conference Paper

VALIANT: Prompt Instability for Active Learning in Black-Box Medical Imaging

Dwarikanath Mahapatra
Behzad Bozorgtabar
Sudipta Roy
Imran Razzak
Mauricio Reyes

The deployment of large, black-box foundation models for medical image classification is often hindered by the high cost of acquiring large, task-specific labeled datasets for fine-tuning. While active learning (AL) presents a promising solution, many state-of-the-art AL methods are computationally expensive or require full access to internal model parameters. We present VALIANT (Visual Adaptation and Learning Integration for Active learNing Tasks), a new active learning framework designed to efficiently adapt black-box foundation models by overcoming these limitations. VALIANT introduces a lightweight Visual Prompt Decoder (VIPD), trained via unsupervised Zero-Order Optimization (ZOO), to generate task-specific visual prompts without internal model access. Our core contribution is a perturbation-based ranking strategy that leverages this VIPD to formulate a computationally efficient, gradient-aware informativeness metric. This metric, which we term prompt instability, identifies the most impactful samples for the labeling budget. VALIANT further enhances this process by incorporating anatomical information from unsupervised segmentation maps to generate more discriminative visual prompts. Extensive evaluations on multiple medical datasets demonstrate VALIANT’s superior performance and significant reduction in labeling costs compared to a range of existing active learning techniques, positioning it as a scalable and practical solution for medical image analysis.

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NeurIPS Conference 2025 Conference Paper

CALM: Culturally Self-Aware Language Models

Lingzhi Shen
Xiaohao Cai
Yunfei Long
Imran Razzak
Guanming Chen
Shoaib Jameel

Cultural awareness in language models is the capacity to understand and adapt to diverse cultural contexts. However, most existing approaches treat culture as static background knowledge, overlooking its dynamic and evolving nature. This limitation reduces their reliability in downstream tasks that demand genuine cultural sensitivity. In this work, we introduce CALM, a novel framework designed to endow language models with cultural self-awareness. CALM disentangles task semantics from explicit cultural concepts and latent cultural signals, shaping them into structured cultural clusters through contrastive learning. These clusters are then aligned via cross-attention to establish fine-grained interactions among related cultural features and are adaptively integrated through a Mixture-of-Experts mechanism along culture-specific dimensions. The resulting unified representation is fused with the model's original knowledge to construct a culturally grounded internal identity state, which is further enhanced through self-prompted reflective learning, enabling continual adaptation and self-correction. Extensive experiments conducted on multiple cross-cultural benchmark datasets demonstrate that CALM consistently outperforms state-of-the-art methods.

NeurIPS Conference 2025 Conference Paper

CG-SSL: Concept-Guided Self-Supervised Learning

Sara Atito
Josef Kittler
Imran Razzak
Muhammad Awais

Humans understand visual scenes by first capturing a global impression and then refining this understanding into distinct, object-like components. Inspired by this process, we introduce \textbf{C}oncept-\textbf{G}uided \textbf{S}elf-\textbf{S}upervised \textbf{L}earning (CG-SSL), a novel framework that brings structure and interpretability to representation learning through a curriculum of three training phases: (1) global scene encoding, (2) discovery of visual concepts via tokenised cross-attention, and (3) alignment of these concepts across views. Unlike traditional SSL methods, which simply enforce similarity between multiple augmented views of the same image, CG-SSL accounts for the fact that these views may highlight different parts of an object or scene. To address this, our method establishes explicit correspondences between views and aligns the representations of meaningful image regions. At its core, CG-SSL augments standard SSL with a lightweight decoder that learns and refines concept tokens via cross-attention with patch features. The concept tokens are trained using masked concept distillation and a feature-space reconstruction objective. A final alignment stage enforces view consistency by geometrically matching concept regions under heavy augmentation, enabling more compact, robust, and disentangled representations of scene regions. Across multiple backbone sizes, CG-SSL achieves state-of-the-art results on image segmentation benchmarks using $k$-NN and linear probes, substantially outperforming prior methods and approaching, or even surpassing, the performance of leading SSL models trained on over $100\times$ more data. Code and pretrained models will be released.

NeurIPS Conference 2025 Conference Paper

Decoding Causal Structure: End-to-End Mediation Pathways Inference

Yulong Li
Xiwei Liu
Feilong Tang
Ming Hu
Jionglong Su
Zongyuan Ge
Imran Razzak
Eran Segal

Causal mediation analysis is crucial for deconstructing complex mechanisms of action. However, in current mediation analysis, complex structures derived from causal discovery lack direct interpretation of mediation pathways, while traditional mediation analysis and effect estimation are limited by the reliance on pre-specified pathways, leading to a disconnection between structure discovery and causal mechanism understanding. Therefore, a unified framework integrating structure discovery, pathway identification, and effect estimation systematically quantifies mediation pathways under structural uncertainty, enabling automated identification and inference of mediation pathways. To this end, we propose Structure-Informed Guided Mediation Analysis (SIGMA), which guides automated mediation pathway identification through probabilistic causal structure discovery and uncertainty quantification, enabling end-to-end propagation of structural uncertainty from structure learning to effect estimation. Specifically, SIGMA employs differentiable Flow-Structural Equation Models to learn structural posteriors, generating diverse Directed Acyclic Graphs (DAGs) to quantify structural uncertainty. Based on these DAGs, we introduce the Path Stability Score to evaluate the marginal probability of pathways, identifying high-confidence mediation paths. For identified mediation pathways, we integrate Efficient Influence Functions with Bayesian model averaging to fuse within-structure estimation uncertainty and between-structure effect variation, propagating uncertainty to the final effect estimates. In synthetic data experiments, SIGMA achieves state-of-the-art performance in pathway identification accuracy and effect quantification precision under structures uncertainty, concurrent multiple pathways, and nonlinear scenarios. In real-world applications using Human Phenotype Project data, SIGMA identifies mediation effects of sleep quality on cardiovascular health through inflammatory and metabolic pathways, uncovering previously unspecified multiple mediation paths.

NeurIPS Conference 2025 Conference Paper

Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning

Ankan Deria
Adinath Dukre
Feilong Tang
Sara Atito
Sudipta Roy
Muhammad Awais
Muhammad Haris Khan
Imran Razzak

Despite significant advances in inference-time search for vision–language models (VLMs), existing approaches remain both computationally expensive and prone to unpenalized, low-confidence generations which often lead to persistent hallucinations. We introduce \textbf{Value-guided Inference with Margin-based Reward (ViMaR)}, a two-stage inference framework that improves both efficiency and output fidelity by combining a temporal-difference value model with a margin-aware reward adjustment. In the first stage, we perform a single pass to identify the highest-value caption among diverse candidates. In the second stage, we selectively refine only those segments that were overlooked or exhibit weak visual grounding, thereby eliminating frequently rewarded evaluations. A calibrated margin-based penalty discourages low-confidence continuations while preserving descriptive richness. Extensive experiments across multiple VLM architectures demonstrate that ViMaR generates captions that are significantly more reliable, factually accurate, detailed, and explanatory, while achieving over 4$\times$ speedup compared to existing value-guided methods. Specifically, we show that ViMaR trained solely on LLaVA Mistral-7B \textit{generalizes effectively to guide decoding in stronger unseen models}. To further validate this, we adapt ViMaR to steer generation in both LLaVA-OneVision-Qwen2-7B and Qwen2. 5-VL-3B, leading to consistent improvements in caption quality and demonstrating robust cross-model guidance. This cross-model generalization highlights ViMaR's flexibility and modularity, positioning it as a scalable and transferable inference-time decoding strategy. Furthermore, when ViMaR-generated captions are used for self-training, the underlying models achieve substantial gains across a broad suite of visual comprehension benchmarks, underscoring the potential of fast, accurate, and self-improving VLM pipelines. Code: https: //github. com/ankan8145/ViMaR

ICRA Conference 2025 Conference Paper

RipGAN: A GAN-Based Rip Current Data Augmentation Method

Shenyang Qian
Mitchell Harley
Imran Razzak
Yang Song 0001

Rip currents are a major hazard on beaches worldwide, and their strong, offshore-directed currents can place even experienced beachgoers at risk of drowning. While it is intuitive to consider developing an automated rip current detection system to assist lifeguards in protecting beachgoers, rip current detection is in its infancy due to the lack of high-quality large-scale annotated rip current datasets. Also, the collection and annotation of rip current images require expert knowledge, which makes it more difficult to build datasets. So, this paper proposes a GAN-based rip current data augmentation method, RipGAN, to improve the performance of rip current detectors by increasing representative training data. To create new training images, RipGAN, has two branches. One is a texture generator that enriches the pattern and texture details of waves, making the image more realistic. The other is a rip generator based on FFFM-Unet. FFFM (Fast Fourier Fusion Module) uses Fast Fourier convolution to fuse the features from the low and the high layers, so as to further optimise the generated image. Furthermore, we trained Yolov8, YOLOv10, DINO and RT-DETR as rip current detectors to prove the effectiveness of RipGAN. The detectors' rnAP 50: 95 improved by 2. 67% on the test set and AP 50 by 4. 93% on real-scene videos, outperforming other data augmentation methods. Besides, abundant ablation studies have been conducted to further evaluate each component of RipGAN.

NeurIPS Conference 2025 Conference Paper

Towards Dynamic 3D Reconstruction of Hand-Instrument Interaction in Ophthalmic Surgery

Ming Hu
Zhengdi Yu
Feilong Tang
Kaiwen Chen
Yulong Li
Imran Razzak
Junjun He
Tolga Birdal

Accurate 3D reconstruction of hands and instruments is critical for vision-based analysis of ophthalmic microsurgery, yet progress has been hampered by the lack of realistic, large-scale datasets and reliable annotation tools. In this work, we introduce OphNet-3D, the first extensive RGB-D dynamic 3D reconstruction dataset for ophthalmic surgery, comprising 41 sequences from 40 surgeons and totaling 7. 1 million frames, with fine-grained annotations of 12 surgical phases, 10 instrument categories, dense MANO hand meshes, and full 6-DoF instrument poses. To scalably produce high-fidelity labels, we design a multi-stage automatic annotation pipeline that integrates multi-view data observation, data-driven motion prior with cross-view geometric consistency and biomechanical constraints, along with a combination of collision-aware interaction constraints for instrument interactions. Building upon OphNet-3D, we establish two challenging benchmarks—bimanual hand pose estimation and hand–instrument interaction reconstruction—and propose two dedicated architectures: H-Net for dual-hand mesh recovery and OH-Net for joint reconstruction of two-hand–two-instrument interactions. These models leverage a novel spatial reasoning module with weak-perspective camera modeling and collision-aware center-based representation. Both architectures outperform existing methods by substantial margins, achieving improvements of over 2mm in Mean Per Joint Position Error (MPJPE) and up to 23\% in ADD-S metrics for hand and instrument reconstruction, respectively.

NeurIPS Conference 2025 Conference Paper

UniViT: Unifying Image and Video Understanding in One Vision Encoder

Feilong Tang
xiangan xiangan
Haolin Yang
Yin Xie
Kaicheng Yang
Ming Hu
Zheng Cheng
Xingyu Zhou

Despite the impressive progress of recent pretraining methods on multimodal tasks, existing methods are inherently biased towards either spatial modeling (e. g. , CLIP) or temporal modeling (e. g. , V-JEPA), limiting their joint capture of spatial details and temporal dynamics. To this end, we propose UniViT, a cluster-driven unified self-supervised learning framework that effectively captures the structured semantics of both image spatial content and video temporal dynamics through event-level and object-level clustering and discrimination. Specifically, we leverage offline clustering to generate semantic clusters across both modalities. For videos, multi-granularity event-level clustering progressively expands from single-event to structured multi-event segments, capturing coarse-to-fine temporal semantics; for images, object-level clustering captures fine-grained spatial semantics. However, while global clustering provides semantically consistent clusters, it lacks modeling of structured semantic relations (e. g. , temporal event structures). To address this, we introduce a contrastive objective that leverages these semantic clusters as pseudo-label supervision to explicitly enforce structural constraints, including temporal event relations and spatial object co-occurrences, capturing structured semantics beyond categories. Meanwhile, UniViT jointly embeds structured object-level and event-level semantics into a unified representation space. Furthermore, UniViT introduces two key components: (i) Unified Rotary Position Embedding integrates relative positional embedding with frequency-aware dimension allocation to support position-invariant semantic learning and enhance the stability of structured semantics in the discrimination stage; and (ii) Variable Spatiotemporal Streams adapt to inputs of varying frame lengths, addressing the rigidity of conventional fixed-input approaches. Extensive experiments across varying model scales demonstrate that UniViT achieves state-of-the-art performance on linear probing, attentive probing, question answering, and spatial understanding tasks.

JBHI Journal 2024 Journal Article

3D-IncNet: Head and Neck (H&N) Primary Tumors Segmentation and Survival Prediction

Abdul Qayyum
Abdesslam Benzinou
Imran Razzak
Moona Mazher
Thanh Thi Nguyen
Domenec Puig
Fatemeh Vafaee

Cancer begins when healthy cells change and grow out of control, forming a mass called a tumor. Head and neck (H&N) cancers usually develop in or around the head and neck, including the mouth (oral cavity), nose and sinuses, throat (pharynx), and voice box (larynx). 4% of all cancers are H&N cancers with a very low survival rate (a five-year survival rate of 64. 7%). FDG-PET/CT imaging is often used for early diagnosis and staging of H&N tumors, thus improving these patients' survival rates. This work presents a novel 3D-Inception-Residual aided with 3D depth-wise convolution and squeeze and excitation block. We introduce a 3D depth-wise convolution-inception encoder consisting of an additional 3D squeeze and excitation block and a 3D depth-wise convolution-based residual learning decoder (3D-IncNet), which not only helps to recalibrate the channel-wise features but adaptively through explicit inter-dependencies modeling but also integrate the coarse and fine features resulting in accurate tumor segmentation. We further demonstrate the effectiveness of inception-residual encoder-decoder architecture in achieving better dice scores and the impact of depth-wise convolution in lowering the computational cost. We applied random forest for survival prediction on deep, clinical, and radiomics features. Experiments are conducted on the benchmark HECKTOR21 challenge, which showed significantly better performance by surpassing the state-of-the-artwork and achieved 0. 836 and 0. 811 concordance index and dice scores, respectively. We made the model and code publicly available.

JBHI Journal 2024 Journal Article

A Novel Collaborative SRU Network With Dynamic Behaviour Aggregation, Reduced Communication Overhead and Explainable Features

Izhar Ahmed Khan
Imran Razzak
Dechang Pi
Umar Zia
Shaharyar Kamal
Yasir Hussain

Leakage and tampering problems in collection and transmission of biomedical data have attracted much attention as these concerns instigates negative impression regarding privacy, security, and reputation of medical networks. This article presents a novel security model that establishes a threat-vector database based on the dynamic behaviours of smart healthcare systems. Then, an improved and privacy-preserved SRU network is designed that aims to alleviate fading gradient issue and enhance the learning process by reducing computational cost. Then, an intelligent federated learning algorithm is deployed to enable multiple healthcare networks to form a collaborative security model in a personalized manner without the loss of privacy. The proposed security method is both parallelizable and computationally effective since the dynamic behaviour aggregation strategy empowers the model to work collaboratively and reduce communication overhead by dynamically adjusting the number of participating clients. Additionally, the visualization of the decision process based on the explainability of features enhances the understanding of security experts by enabling them to comprehend the underlying data evidence and causal reasoning. Compared to existing methods, the proposed security method is capable of thoroughly analyzing and detecting severe security threats with high accuracy, reduce overhead and lower computation cost along with enhanced privacy of biomedical data.

NeurIPS Conference 2024 Conference Paper

Building Timeseries Dataset: Empowering Large-Scale Building Analytics

Arian Prabowo
Xiachong Lin
Imran Razzak
Hao Xue
Emily W. Yap
Matthew Amos
Flora D. Salim

Buildings play a crucial role in human well-being, influencing occupant comfort, health, and safety. Additionally, they contribute significantly to global energy consumption, accounting for one-third of total energy usage, and carbon emissions. Optimizing building performance presents a vital opportunity to combat climate change and promote human flourishing. However, research in building analytics has been hampered by the lack of accessible, available, and comprehensive real-world datasets on multiple building operations. In this paper, we introduce the Building TimeSeries (BTS) dataset. Our dataset covers three buildings over a three-year period, comprising more than ten thousand timeseries data points with hundreds of unique ontologies. Moreover, the metadata is standardized using the Brick schema. To demonstrate the utility of this dataset, we performed benchmarks on two tasks: timeseries ontology classification and zero-shot forecasting. These tasks represent an essential initial step in addressing challenges related to interoperability in building analytics. Access to the dataset and the code used for benchmarking are available here: https: //github. com/cruiseresearchgroup/DIEF_BTS

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NeurIPS Conference 2024 Conference Paper

Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model

Yanpeng Ye
Jie Ren
Shaozhou Wang
Yuwei Wan
Imran Razzak
Bram Hoex
Haofeng Wang
Tong Xie

Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges for efficient discovery and integration of new materials. Traditional methods, often reliant on costly and time-consuming experimental approaches, further complicate rapid innovation. Addressing these challenges, the integration of artificial intelligence with materials science has opened avenues for accelerating the discovery process, though it also demands precise annotation, data extraction, and traceability of information. To tackle these issues, this article introduces the Materials Knowledge Graph (MKG), which utilizes advanced natural language processing techniques, integrated with large language models to extract and systematically organize a decade's worth of high-quality research into structured triples, contains 162, 605 nodes and 731, 772 edges. MKG categorizes information into comprehensive labels such as Name, Formula, and Application, structured around a meticulously designed ontology, thus enhancing data usability and integration. By implementing network-based algorithms, MKG not only facilitates efficient link prediction but also significantly reduces reliance on traditional experimental methods. This structured approach not only streamlines materials research but also lays the groundwork for more sophisticated materials knowledge graphs.

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AAAI Conference 2024 Conference Paper

DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning

Jincen Jiang
Lizhi Zhao
Xuequan Lu
Wei Hu
Imran Razzak
Meili Wang

Recent works attempt to extend Graph Convolution Networks (GCNs) to point clouds for classification and segmentation tasks. These works tend to sample and group points to create smaller point sets locally and mainly focus on extracting local features through GCNs, while ignoring the relationship between point sets. In this paper, we propose the Dynamic Hop Graph Convolution Network (DHGCN) for explicitly learning the contextual relationships between the voxelized point parts, which are treated as graph nodes. Motivated by the intuition that the contextual information between point parts lies in the pairwise adjacent relationship, which can be depicted by the hop distance of the graph quantitatively, we devise a novel self-supervised part-level hop distance reconstruction task and design a novel loss function accordingly to facilitate training. In addition, we propose the Hop Graph Attention (HGA), which takes the learned hop distance as input for producing attention weights to allow edge features to contribute distinctively in aggregation. Eventually, the proposed DHGCN is a plug-and-play module that is compatible with point-based backbone networks. Comprehensive experiments on different backbones and tasks demonstrate that our self-supervised method achieves state-of-the-art performance. Our source codes are available at: https://github.com/Jinec98/DHGCN.

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JBHI Journal 2024 Journal Article

Guest Editorial Advanced Machine Learning and Artificial Intelligence Tools for Computational Biology: Methodologies and Challenges

Fahmi Khalifa
Imran Razzak
Mohammad Amjad Kamal
Ahmed Soliman

In recent years, the management and analysis of biological data have experienced exponential growth propelled by the relentless advancement of machine learning (ML) and artificial intelligence (AI) technologies. This is driven mainly by the remarkable ability and potentials of AI-based systems to craft sophisticated, yet effective, algorithms and analytical models tailored for the interpretation of biological information; thus, assist in making accurate predictions and/or decisions [1]. The surge in AI adoption is not unfounded; it's a response to the overwhelming increase in both the volume and acquisition rates of biological data.

JBHI Journal 2024 Journal Article

Hierarchical Convolutional Attention Network for Depression Detection on Social Media and Its Impact During Pandemic

Hamad Zogan
Imran Razzak
Shoaib Jameel
Guandong Xu

People across the globe have felt and are still going through the impact of COVID-19. Some of them share their feelings and suffering online via different online social media networks such as Twitter. Due to strict restrictions to reduce the spread of the novel virus, many people are forced to stay at home, which significantly impacts people's mental health. It is mainly because the pandemic has directly affected the lives of the people who were not allowed to leave home due to strict government restrictions. Researchers must mine the related human-generated data and get insights from it to influence government policies and address people's needs. In this paper, we study social media data to understand how COVID-19 has impacted people's depression. We share a large-scale COVID-19 dataset that can be used to analyze depression. We also have modeled the tweets of depressed and non-depressed users before and after the start of the COVID-19 pandemic. To this end, we developed a new approach based on Hierarchical Convolutional Neural Network (HCN) that extracts fine-grained and relevant content on user historical posts. HCN considers the hierarchical structure of user tweets and contains an attention mechanism that can locate the crucial words and tweets in a user document while also considering the context. Our new approach is capable of detecting depressed users occurring within the COVID-19 time frame. Our results on benchmark datasets show that many non-depressed people became depressed during the COVID-19 pandemic.

JBHI Journal 2024 Journal Article

LDMRes-Net: A Lightweight Neural Network for Efficient Medical Image Segmentation on IoT and Edge Devices

Shahzaib Iqbal
Tariq M. Khan
Syed S. Naqvi
Asim Naveed
Muhammad Usman
Haroon Ahmed Khan
Imran Razzak

In this study, we propose LDMRes-Net, a lightweight dual-multiscale residual block-based convolutional neural network tailored for medical image segmentation on IoT and edge platforms. Conventional U-Net-based models face challenges in meeting the speed and efficiency demands of real-time clinical applications, such as disease monitoring, radiation therapy, and image-guided surgery. In this study, we present the Lightweight Dual Multiscale Residual Block-based Convolutional Neural Network (LDMRes-Net), which is specifically designed to overcome these difficulties. LDMRes-Net overcomes these limitations with its remarkably low number of learnable parameters (0. 072 M), making it highly suitable for resource-constrained devices. The model's key innovation lies in its dual multiscale residual block architecture, which enables the extraction of refined features on multiple scales, enhancing overall segmentation performance. To further optimize efficiency, the number of filters is carefully selected to prevent overlap, reduce training time, and improve computational efficiency. The study includes comprehensive evaluations, focusing on the segmentation of the retinal image of vessels and hard exudates crucial for the diagnosis and treatment of ophthalmology. The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms. Such advances hold significant promise for improving healthcare outcomes and enabling real-time medical image analysis in resource-limited settings.

AIIM Journal 2024 Journal Article

LSSF-Net: Lightweight segmentation with self-awareness, spatial attention, and focal modulation

Hamza Farooq
Zuhair Zafar
Ahsan Saadat
Tariq M. Khan
Shahzaib Iqbal
Imran Razzak

Accurate segmentation of skin lesions within dermoscopic images plays a crucial role in the timely identification of skin cancer for computer-aided diagnosis on mobile platforms. However, varying shapes of the lesions, lack of defined edges, and the presence of obstructions such as hair strands and marker colours make this challenge more complex. Additionally, skin lesions often exhibit subtle variations in texture and colour that are difficult to differentiate from surrounding healthy skin, necessitating models that can capture both fine-grained details and broader contextual information. Currently, melanoma segmentation models are commonly based on fully connected networks and U-Nets. However, these models often struggle with capturing the complex and varied characteristics of skin lesions, such as the presence of indistinct boundaries and diverse lesion appearances, which can lead to suboptimal segmentation performance. To address these challenges, we propose a novel lightweight network specifically designed for skin lesion segmentation utilising mobile devices, featuring a minimal number of learnable parameters (only 0. 8 million). This network comprises an encoder–decoder architecture that incorporates conformer-based focal modulation attention, self-aware local and global spatial attention, and split channel-shuffle. The efficacy of our model has been evaluated on four well-established benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, ISIC 2018, and PH2. Empirical findings substantiate its state-of-the-art performance, notably reflected in a high Jaccard index.

EAAI Journal 2024 Journal Article

PCA: Progressive class-wise attention for skin lesions diagnosis

Asim Naveed
Syed S. Naqvi
Tariq M. Khan
Imran Razzak

Skin cancer is the most prevalent type of cancer worldwide. Early detection is essential as it could be fatal at later stages. The classification of skin lesions is challenging since there are many variations, including changes in color, shape, size, high intra-class variation, and high inter-class similarity. In this paper, a unique class-wise attention method is proposed that considers each class equally while extracting additional discriminative information of skin lesions. The proposed attention mechanism is employed in a progressive manner to incorporate discriminative feature information from multiple scales. The proposed approach obtained competitive performance against more than 15 state-of-the-art methods including HAM1000 and ISIC 2019 leaderboard winners. The proposed method achieved 97. 40% accuracy on the HAM10000 and 94. 9% accuracy on the ISIC 2019 dataset.

JBHI Journal 2023 Journal Article

Conv-eRVFL: Convolutional Neural Network Based Ensemble RVFL Classifier for Alzheimer's Disease Diagnosis

Rahul Sharma
Tripti Goel
M Tanveer
P. N. Suganthan
Imran Razzak
R Murugan

As per the latest statistics, Alzheimer's disease (AD) has become a global burden over the following decades. Identifying AD at the intermediate stage became challenging, with mild cognitive impairment (MCI) utilizing credible biomarkers and robust learning approaches. Neuroimaging techniques like magnetic resonance imaging (MRI) and positron emission tomography (PET) are practical research approaches that provide structural atrophies and metabolic variations. With the help of MRI and PET scans, metabolic and structural changes in AD patients can be visible even ten years before the disease's onset. This paper proposes a novel wavelet packet transform-based structural and metabolic image fusion approach using MRI and PET scans. An eight-layer trained CNN extracts features from multiple layers and these features are fed to an ensemble of non-iterative random vector functional link (RVFL) models. The RVFL network incorporates the $s$ -membership fuzzy function as an activation function that helps overcome outliers. Lastly, outputs of all the customized RVFL classifiers are averaged and fed to the RVFL classifier to make the final decision. Experiments are performed over Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and classification is made over CN vs. AD vs. MCI. The model performance obtained is decent enough to prove the effectiveness of the fusion-based ensemble approach.

JBHI Journal 2023 Journal Article

Federated Learning for Privacy Preservation of Healthcare Data From Smartphone-Based Side-Channel Attacks

Abdul Rehman
Imran Razzak
Guandong Xu

Federated learning (FL) has recently emerged as a striking framework for allowing machine and deep learning models with thousands of participants to have distributed training to preserve the privacy of users’ data. Federated learning comes with the pros of allowing all participants the possibility of creating robust models even in the absence of sufficient training data. Recently, smartphone usage has increased significantly due to its portability and ability to perform many daily life tasks. Typing on a smartphone’s soft keyboard generates vibrations that could be abused to detect the typed keys, aiding side-channel attacks. Such data can be collected using smartphone hardware sensors during the entry of sensitive information such as clinical notes, personal medical information, username, and passwords. This study proposes a novel framework based on federated learning for side-channel attack detection to secure this information. We collected a dataset from 10 Android smartphone users who were asked to type on the smartphone soft keyboard. We convert this dataset into two windows of five users to make two clients training local models. The federated learning-based framework aggregates model updates contributed by two clients and trained the Deep Neural Network (DNN) model individually on the dataset. To reduce the over-fitting factor, each client examines the findings three times. Experiments reveal that the DNN model achieves an accuracy of 80. 09%, showing that the proposed framework has the potential to detect side-channel attacks.

JBHI Journal 2023 Journal Article

Guest Editorial Open and Interpretable AI in Computational Pathology

Imran Razzak
Muhammad Khuram Khan
Guandong Xu
Fahmi Khalifa

The thirteen papers in this special issue focus on open and interoperable artificial intelligence (AI) in computational pathology. Recent years have seen exponential advances in the quality of AI techniques in the medical filed. Particularly, AI/ML tools have been widely exploited pathological image analysis to examine and assess the function of human organs and/or to provide trustworthy prediction of diseases.

JBHI Journal 2023 Journal Article

Restricted Boltzmann Machine Assisted Secure Serverless Edge System for Internet of Medical Things

Abdullah Lakhan
Mazin Abed Mohammed
Ahmed N. Rashid
Seifedine Kadry
Karrar Hameed Abdulkareem
Jan Nedoma
Radek Martinek
Imran Razzak

The Internet of things (IoT) is a network of technologies that support a wide variety of healthcare workflow applications to facilitate users’ obtaining real-time healthcare services. Many patients and doctors’ hospitals use different healthcare services to monitor their healthcare and save their records on the servers. Healthcare sensors are widely linked to the outside world for different disease classifications and questions. These applications are extraordinarily dynamic and use mobile devices to roam several locales. However, healthcare apps confront two significant challenges: data privacy and the cost of application execution services. This work presents the mobility-aware security dynamic service composition (MSDSC) algorithmic framework for workflow healthcare based on serverless, serverless, and restricted Boltzmann machine mechanisms. The study suggests the stochastic deep neural network trains probabilistic models at each phase of the process, including service composition, task sequencing, security, and scheduling. The experimental setup and findings revealed that the developed system-based methods outperform traditional methods by 25% in terms of safety and 35% in application cost.

EAAI Journal 2023 Journal Article

Semi-supervised 3D-InceptionNet for segmentation and survival prediction of head and neck primary cancers

Abdul Qayyum
Moona Mazher
Tariq Khan
Imran Razzak

Cancers, known collectively as head and neck cancers, usually begin in the squamous cells that line the head and neck’s mucosal surfaces, forming a tumour mass. It usually develops in the salivary glands, nose and sinuses, voice box (larynx), throat (pharynx), or muscles or nerves in the head and neck, but these types of cancer are much less common than squamous cell carcinomas. Nearly 4% of all cancers are H&N cancers with a very low survival rate (a five-year survival rate of 64. 7%). The most commonly used molecular imaging procedure for diagnosing or guiding the treatment of head and neck cancer is Fluorodeoxyglucose-positron emission tomography scanning (FDG-PET/CT), which is often used in conjunction with computed tomography (CT) scanning, and sentinel node biopsy. This work presents a semi-supervised 3D Inception-Residual framework with 3D depth-wise convolution and squeeze and excitation block. In the first phase, we performed pre-training of 3D-auto-encoder using both train and test unlabelled dataset. We, then used pre-trained weight to fine-tune the later network which is aided with depth-wise convolution-inception encoder consisting of an additional 3D squeeze and excitation block and a 3D depth-wise convolution-based residual learning decoder under deep supervision ( Semi 3D-IncNet ). The proposed network not only helps to recalibrate the channel-wise features adaptively through explicit inter-dependencies modelling but also integrates the coarse and fine features resulting in accurate tumour segmentation. We further demonstrate the effectiveness of semi-supervised inception-residual encoder–decoder architecture in achieving better dice scores and the impact of depth-wise convolution in lowering the computational cost. For survival prediction, we applied random forest on deep, clinical, and radiomics features. Experiments were conducted on the benchmark HECKTOR2021 and HECKTOR2022 challenge showed significantly better performance by surpassing the state-of-the-artwork and achieved 0. 824/0. 836 and 0. 754/0. 678 Dice/Concordance index for HECKTOR2021 and HECKTOR2022 respectively. We made the model and code publicly available. 1 1 https: //github. com/RespectKnowledge/HeadandNeck21_3D_Segmentation.

EAAI Journal 2023 Journal Article

Simple and robust depth-wise cascaded network for polyp segmentation

Tariq M. Khan
Muhammad Arsalan
Imran Razzak
Erik Meijering

The segmentation of the polyp region in colonoscopy images is considered difficult due to size, texture, and color variation. To segment polyps successfully, models based on convolutional neural networks (CNN), transformers, and their combinations have been developed. However, these methods are limited in that they can only model the local appearance of polyps or lack multi-level feature representation for spatial dependency in the decoding process. In this paper, we propose a simple, efficient yet powerful polyp segmentation framework that unifies the network with a multiscale cascaded path. The proposed MMS-Net utilizes multiscale and multipath convolutional operations in conjunction with multiple deep feature aggregation. The overall dense empowered features are sufficient for pixel-by-pixel detection of the polyp region. Extensive experiments on two popular benchmark datasets for polyp segmentation (Kvasir and CVC-Clinic DB) and two datasets of other medical applications (DRIVE and MC) are presented. The results show that our MMS-Net performs comparably to or better than other state-of-the-art methods despite having two or even three orders of magnitude fewer trainable parameters.

JBHI Journal 2022 Journal Article

Classification of Alzheimer’s Disease Using Ensemble of Deep Neural Networks Trained Through Transfer Learning

M. Tanveer
A. H. Rashid
M. A. Ganaie
M. Reza
Imran Razzak
Kai-Lung Hua

Alzheimer’s disease (AD) is one of the deadliest neurodegenerative diseases ailing the elderly population all over the world. An ensemble of Deep learning (DL) models can learn highly complicated patterns from MRI scans for the detection of AD by utilizing diverse solutions. In this work, we propose a computationally efficient, DL-architecture agnostic, ensemble of deep neural networks, named ‘Deep Transfer Ensemble (DTE)’ trained using transfer learning for the classification of AD. DTE leverages the complementary feature views and diversity introduced by many different locally optimum solutions reached by individual networks through the randomization of hyper-parameters. DTE achieves an accuracy of 99. 05% and 85. 27% on two independent splits of the large dataset for cognitively normal (NC) vs AD classification task. For the task of mild cognitive impairment (MCI) vs AD classification, DTE achieves 98. 71% and 83. 11% respectively on the two independent splits. It also performs reasonable on a small dataset consisting of only 50 samples per class. It achieved a maximum accuracy of 85% for NC vs AD on the small dataset. It also outperformed snapshot ensembles along with several other existing deep models from similar kind of previous works by other researchers.

JBHI Journal 2022 Journal Article

Edge Intelligence: Federated Learning-Based Privacy Protection Framework for Smart Healthcare Systems

Mahmuda Akter
Nour Moustafa
Timothy Lynar
Imran Razzak

Federated learning methods offer secured monitor services and privacy-preserving paradigms to end-users and organisations in the Internet of Things networks such as smart healthcare systems. Federated learning has been coined to safeguard sensitive data, and its global aggregation is often based on a centralised server. This design is vulnerable to malicious attacks and could be breached by privacy attacks such as inference and free-riding, leading to inefficient training models. Besides, uploaded analysing parameters by patients can reveal private information and the threat of direct manipulation by the central server. To address these issues, we present a three-fold Federated Edge Aggregator, the so-called Edge Intelligence, a federated learning-based privacy protection framework for safeguarding Smart Healthcare Systems at the edge against such privacy attacks. We employ an iteration-based Conventional Neural Network (CNN) model and artificial noise functions to balance privacy protection and model performance. A theoretical convergence bound of Edge Intelligence on the trained federated learning model's loss function is also introduced here. We evaluate and compare the proposed framework with the recently established methods using model performance and privacy budget on popular and recent datasets: MNIST, CIFAR10, STL10, and COVID19 chest x-ray. Finally, the proposed framework achieves 90% accuracy and a high privacy rate demonstrating better performance than the baseline technique.

JBHI Journal 2022 Journal Article

Lightweight Face Anti-Spoofing Network for Telehealth Applications

Jiun-Da Lin
Hung-Hsiang Lin
Jilyan Dy
Jun-Cheng Chen
M. Tanveer
Imran Razzak
Kai-Lung Hua

Online healthcare applications have grown more popular over the years. For instance, telehealth is an online healthcare application that allows patients and doctors to schedule consultations, prescribe medication, share medical documents, and monitor health conditions conveniently. Apart from this, telehealth can also be used to store a patient's personal and medical information. With its rise in usage due to COVID-19, given the amount of sensitive data it stores, security measures are necessary. A simple way of making these applications more secure is through user authentication. One of the most common and often used authentications is face recognition. It is convenient and easy to use. However, face recognition systems are not foolproof. They are prone to malicious attacks like printed photos, paper cutouts, replayed videos, and 3D masks. The goal of face anti-spoofing is to differentiate real users (live) from attackers (spoof). Although effective in terms of performance, existing methods use a significant amount of parameters, making them resource-heavy and unsuitable for handheld devices. Apart from this, they fail to generalize well to new environments like changes in lighting or background. This paper proposes a lightweight face anti-spoofing framework that does not compromise on performance. Our proposed method achieves good performance with the help of an ArcFace Classifier (AC). The AC encourages differentiation between spoof and live samples by making clear boundaries between them. With clear boundaries, classification becomes more accurate. We further demonstrate our model's capabilities by comparing the number of parameters, FLOPS, and performance with other state-of-the-art methods.

JBHI Journal 2022 Journal Article

Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation

Iman Beheshti
M. A. Ganaie
Vardhan Paliwal
Aryan Rastogi
Imran Razzak
M. Tanveer

Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not been comprehensively evaluated. Here, we sought to assess the efficiency of different regression algorithms on brain age estimation. To this end, we built a brain age estimation framework based on a large set of cognitively healthy (CH) individuals ( $N = 788$ ) as a training set followed by different regression algorithms (22 different algorithms in total). We then quantified each regression-algorithm on independent test sets composed of 88 CH individuals, 70 mild cognitive impairment patients as well as 30 Alzheimer’s disease patients. The prediction accuracy in the independent test set (i. e. , CH set) varied in regression algorithms mean absolute error (MAE) from 4. 63 to 7. 14 yrs, $R^2$ from 0. 76 to 0. 88. The highest and lowest prediction accuracies were achieved by Quadratic Support Vector Regression algorithm (MAE $= 4. 63$ yrs, $R^2 = 0. 88, 95\%$ CI $= [-1. 26, 1. 42]$ ) and Binary Decision Tree algorithm (MAE $= 7. 14$ yrs, $R^2 = 0. 76, 95\%$ CI $= [-1. 50, 2. 62]$ ), respectively. Our experimental results demonstrate that the prediction accuracy in brain age frameworks is affected by regression algorithms, indicating that advanced machine learning algorithms can lead to more accurate brain age predictions in clinical settings.