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Shaohua Wan

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13 papers

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AAAI Conference 2026 Conference Paper

CasMoE: A Cascaded Framework for Efficient MoE Inference on Resource-constrained Devices

Chengcheng Wang
Haowen He
Liang Zhao
Xiaoheng Deng
Lixin Duan
Shaohua Wan

The Mixture-of-Experts (MoE) architecture has emerged as a key enabler for scaling large language models (LLMs), empowering increased model capacity with minimal computational overhead through gating-based dynamic expert activation. However, due to the memory demands introduced by expert modules, MoE inference on resource-constrained devices is still challenging. Existing methods such as model compression and parameter offloading provide partial alleviation but often lead to reduced accuracy or increased latency. In this paper, we propose CasMoE, a general and efficient cascaded framework for accelerating MoE inference on resource-constrained devices. CasMoE employs a two-stage offline-online approach to facilitate efficient expert prefetching. In the offline stage, a parameterized Expert Activation Predictor (EAP) is introduced to accurately predict the corresponding expert activation from the incoming prompt. In the online stage, a non-parametric Expert Activation Matcher (EAM) supporting fast expert retrieval is then integrated with the EAP to form a cascade planner that operates independently of the MoE architecture, predicting activated experts for all MoE layers in a single pass prior to decoding. A gating mechanism is also incorporated to dynamically adjust the sensitivity of the EAM and EAP, enabling a flexible trade-off between inference efficiency and quality. Extensive experiments on diverse downstream tasks demonstrate CasMoE’s effectiveness in accelerating inference while preserving high accuracy.

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JBHI Journal 2026 Journal Article

DT-VNet: Deep Transformer-Based VNet Framework for 3D Prostate MRI Segmentation

Yunyao Cai
Hu Lu
Shengli Wu
Stefano Berretti
Shaohua Wan

Magnetic ResonanceImaging (MRI) is widely used in examining and diagnosing prostate diseases due to its high resolution. However, the diverse morphology of prostate tissue presents a significant challenge for precise gland segmentation. Convolutional Neural Networks have demonstrated effectiveness in segmenting prostate regions. Nevertheless, their limited capability in extracting global long-range semantic features often leads to unstable network segmentation performance. To address these challenges, we propose a Deep Transformer-based Vnet framework (DT-VNet), which consists of a symmetric encoder-decoder architecture that explores global contextual features and retains local feature information. To effectively learn global and local features, We propose the Deep Union Transformer (DU-Trans) as an encoding base module for capturing comprehensive information. Additionally, we introduce a Pool Fusion Attention (PFA) module for decoding, which emphasizes learning context dependencies and interaction relationships. PFA can also facilitate the fusion of deep and shallow features. To our knowledge, this is the first study about deep transformer-based Vnet framework for prostate segmentation. We validate and compare our method on several public datasets against current state-of-the-art methods. The results demonstrate the superior performance of our proposed method in segmenting 3D prostate MRI.

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TAAS Journal 2026 Journal Article

Federated Meta-Learning for Autonomous System in VEC-Enabled ICVs

Chunlin Li
Kun Jiang
Sihan Zeng
Guangxuan He
Shaohua Wan

Autonomous systems in VEC-enabled ICVs face many challenges, such as self-organization, privacy breach risks, vehicle selection, and resource allocation. As a distributed training framework, Federated Meta-Learning (FML) provides a powerful tool for adaptive and efficient processing of vehicular tasks while securing vehicle data privacy in VEC-enabled ICVs. However, the high-speed mobility of vehicles leads to higher latency and communication interruptions. This article investigates the vehicle selection and resource allocation scheme, subject to the constraints on the number and the residence time of vehicles, the maximum transmission energy consumption, and the ratio of bandwidth resource allocation. It is proved to be a challenging mixed-integer nonlinear programming problem, and we formulate it as a Markov decision process (MDP). We proposed an adaptive Sum Tree-Deep Recurrent Q-network algorithm (ST-DRQN) to solve the optimal resource allocation. ST-DRQN employs an enhanced empirical selection rule and a proportional priority sampling method to address the problems of inefficient model training and slow convergence. Finally, we conducted experiments using intelligent cars equipped with Raspberry Pi to show the effectiveness of the proposed methodology. Experimental results demonstrate that ST-DRQN achieves adaptability and credibility among ICVs while reducing latency and energy costs incurred by long-term training of FML.

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EAAI Journal 2026 Journal Article

Multi-view simulation for robust polyp segmentation via cross-gated decoding and soft-attention fusion

Linbo Wang
Cong Chen
Jinxian Qiu
Zhengyi Liu
Xianyong Fang
Shaohua Wan

Polyp segmentation is crucial for early colorectal cancer detection but remains challenging due to significant shape variations and ambiguous boundaries. Existing methods often rely on single-view analysis, overlooking the potential of multi-view representations to provide complementary segmentation cues. To address this, we propose a novel multi-view simulation-based polyp segmentation network (MVSNet) that generates diverse views of an input image through directional flipping and extracts robust features using a shared Pyramid Vision Transformer (PVT). Additional two tactics are proposed to effectively utilize the rich features from each view and the whole view set respectively: (1) A cross gating based view-aware multi-stage decoding method, which applies the element-wise cross gating to both the coarse and fine features in each stage and thus boosts multi-stage decoded features with high discrimination specific to each individual view for its initial segmentation mask; and (2) a soft-attention based cross-View prediction method, which takes soft attention among different views to adaptively weight the contributions from each view for the final prediction. Extensive experiments on five benchmark datasets (Kvasir-SEG, ClinicDB, ColonDB, ETIS, and Endoscene) validate the effectiveness of our approach, achieving the highest mean Dice scores of 0. 923, 0. 946, 0. 825, 0. 821, and 0. 902 respectively, demonstrating consistent superiority over existing state-of-the-art competitors. Code is available at https: //github. com/linbowang/MVSNet.

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JBHI Journal 2026 Journal Article

Plausible Deniable Medical Image Encryption by Large Language Models and Reversible Content-Aware Strategy

Yirui Wu
Xinfu Liu
Lucia Cascone
Michele Nappi
Shaohua Wan

There is a rising concern about healthcare system security, where data loss could bring lots of damages to patients and hospitals. As a promising encryption method for medical images, DNA encoding own characteristics of high speed, parallelism computation, minimal storage, and unbreakable cryptosystems. Inspired by the idea of involving Large Language Models(LLMs) to improve DNA encoding, we propose a medical image encryption method with LLM-enhanced DNA encoding, which consists of LLM enhancing module and content-aware permutation&diffusion module. Regarding medical images generally have plain backgrounds with low-entropy pixels, the first module compresses pixels into highly compact signals with features of probabilistic varying and plausibly deniability, serving as another LLM-based layer of defense against privacy breaches before DNA encoding. The second module not only adds permutation by randomly sampling from a redundant correlation between adjacent pixels to break the internal links between pixels but also performs a DNA-based diffusion process to greatly increase the complexity of cracking. Experiments on ChestXray-14, COVID-CT and fcon-1000 datasets show that the proposed method outperforms all comparative methods in sensitivity, correlation and entropy.

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AAAI Conference 2025 Conference Paper

Deconfound Semantic Shift and Incompleteness in Incremental Few-shot Semantic Segmentation

Yirui Wu
Yuhang Xia
Hao Li
Lixin Yuan
Junyang Chen
Jun Liu
Tong Lu
Shaohua Wan

Incremental few-shot semantic segmentation (IFSS) expands segmentation capacity of the trained model to segment new-class images with few samples. However, semantic meanings may shift from background to object class or vice versa during incremental learning. Moreover, new-class samples often lack representative attribute features when the new class greatly differs from the pre-learned old class. In this paper, we propose a causal framework to discuss the cause of semantic shift and incompleteness in IFSS, and we deconfound the revealed causal effects from two aspects. First, we propose a Causal Intervention Module (CIM) to resist semantic shift. CIM progressively and adaptively updates prototypes of old class, and removes the confounder in an intervention manner. Second, a Prototype Refinement Module (PRM) is proposed to complete the missing semantics. In PRM, knowledge gained from the episode learning scheme assists in fusing features of new-class and old-class prototypes. Experiments on both PASCAL-VOC 2012 and ADE20k benchmarks demonstrate the outstanding performance of our method.

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TIST Journal 2025 Journal Article

Joint Service Migration and Resource Allocation for DNN Tasks using SA‐DDQN‐DDPG in Vehicular Edge Computing

Chunlin Li
Zihao Zhang
Bingxin Wang
Mengchao Lei
Sen Liu
Aoyong Li
Shaohua Wan

With the rapid development of vehicular edge computing (VEC) and artificial intelligence (AI), the emergence of vehicle edge intelligence meets the need for real-time vehicle intelligence applications. But the execution of deep neural networks (DNNs) requires a large amount of data input, which results in a large amount of computing resources required for the execution of DNN tasks. This also brings a certain burden to the deployment of DNN tasks and the resource allocation of edge servers. In addition, due to the high mobility of vehicles in the VEC, the backhaul delay of vehicle edge intelligent task results increases, affecting the vehicle’s quality of experience (QoE). We propose a joint optimization strategy for service migration and resource allocation aimed at minimizing the average task completion delay. This strategy comprehensively considers service migration actions and edge server resource allocation, which is proved to be a mixed integer nonlinear programming (MINLP) problem, and hence we formulate it as an Markov decision process (MDP). To solve this problem, we propose a service migration algorithm based on the self-attention mechanism-based double deep Q-network and deep deterministic policy gradient algorithm (SA-DDQN-DDPG) to solve it to obtain the optimal system service migration strategy. The experimental results show that the proposed SA-DDQN-DDPG algorithm has good performance in reducing latency. The average migration latency is reduced by 40.41%, 20.7%, and 14.50% compared with always, DQN and DDQN, respectively.

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JBHI Journal 2025 Journal Article

Multimodal Distillation Pre-Training Model for Ultrasound Dynamic Images Annotation

Xiaojun Chen
Jia Ke
Yaning Zhang
Jianping Gou
Anna Shen
Shaohua Wan

With the development of medical technology, ultrasonography has become an important diagnostic method in doctors' clinical work. However, compared with the static medical image processing work such as CT, MRI, etc. , which has more research bases, ultrasonography is a dynamic medical image similar to video, which is captured and generated by a real-time moving probe, so how to deal with the video data in the medical field and cross modal extraction of the textual semantics in the medical video is a difficult problem that needs to be researched. For this reason, this paper proposes a pre-training model of multimodal distillation and fusion coding for processing the semantic relationship between ultrasound dynamic Images and text. Firstly, by designing the fusion encoder, the visual geometric features of tissues and organs in ultrasound dynamic images, the overall visual appearance descriptive features and the named entity linguistic features are fused to form a unified visual-linguistic feature, so that the model obtains richer visual, linguistic cues aggregation and alignment ability. Then, the pre-training model is augmented by multimodal knowledge distillation to improve the learning ability of the model. The final experimental results on multiple datasets show that the multimodal distillation pre-training model generally improves the fusion ability of various types of features in ultrasound dynamic images, and realizes the automated and accurate annotation of ultrasound dynamic images.

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JBHI Journal 2023 Journal Article

A Novel Deep Learning Model for Medical Report Generation by Inter-Intra Information Calibration

Junsan Zhang
Xiuxuan Shen
Shaohua Wan
Sotirios K. Goudos
Jie Wu
Ming Cheng
Weishan Zhang

Automatic generation of medical reports can provide diagnostic assistance to doctors and reduce their workload. To improve the quality of the generated medical reports, injecting auxiliary information through knowledge graphs or templates into the model is widely adopted in previous methods. However, they suffer from two problems: 1) The injected external information is limited in amount and difficult to adequately meet the information needs of medical report generation in content. 2) The injected external information increases the complexity of model and is hard to be reasonably integrated into the generation process of medical reports. Therefore, we propose an Information Calibrated Transformer (ICT) to address the above issues. First, we design a Precursor-information Enhancement Module (PEM), which can effectively extract numerous inter-intra report features from the datasets as the auxiliary information without external injection. And the auxiliary information can be dynamically updated with the training process. Secondly, a combination mode, which consists of PEM and our proposed Information Calibration Attention Module (ICA), is designed and embedded into ICT. In this method, the auxiliary information extracted from PEM is flexibly injected into ICT and the increment of model parameters is small. The comprehensive evaluations validate that the ICT is not only superior to previous methods in the X-Ray datasets, IU-X-Ray and MIMIC-CXR, but also successfully be extended to a CT COVID-19 dataset COV-CTR.

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JBHI Journal 2023 Journal Article

Two Path Gland Segmentation Algorithm of Colon Pathological Image Based on Local Semantic Guidance

Songtao Ding
Hongyu Wang
Hu Lu
Michele Nappi
Shaohua Wan

Colonic adenocarcinoma is a disease severely endangering human life caused by mucosal epidermal carcinogenesis. The segmentation of potentially cancerous glands is the key in the detection and diagnosis of colonic adenocarcinoma. The appearance of cancerous tissue is different in gland segmentation in colon pathological images, and it is impossible to accurately segment the changes of glands from benign to malignant using a single network. Given these issues, a two-path gland segmentation algorithm of colon pathological image based on local semantic guidance is proposed in this paper. The improved candidate region search algorithm is adopted to expand the original image data set and generate sub-datasets sensitive to specific features. Then, the semantic feature-guided model is employed to extract the local adenocarcinoma features and acts on the backbone network together with context feature extraction based on the attention mechanism. In this way, a larger receptive field and more local feature information are obtained, the learning ability of the network to the morphological features of glands is enhanced, and the performance of automatic gland segmentation is finally improved. The algorithm is verified on Warwick Qu-Dataset. Compared with the current popular segmentation algorithms, our algorithm has good performance in Dice coefficient, F1 score, and Hausdorff distance on different types of test sets.

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JBHI Journal 2022 Journal Article

Guest Editorial Emerging IoT-Driven Smart Health: From Cloud to Edge

Shaohua Wan
Michele Nappi
Chen Chen
Stefano Berretti

The papers in this special section focus on emerging Internet of Medical Things. Recent advances in advances in healthcare can be experienced with the development of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), edge computing, Edge AI, 6G, cloud computing, and connected healthcare have attracted a great deal of attention and a wide range of views. However, the need to deliver real-time and accurate healthcare services to patients, while reducing costs is a challenging issue [1]. Especially, COVID-19 has recently demonstrated the importance of fast, comprehensive, and accurate intelligent healthcare involving different types of medical, physiological, and epidemiological investigation data to diagnose the virus. Smart health is a real-time, intelligent, ubiquitous healthcare service based on Internet of bioMedical Things (IoMT). With the rapid development of related technologies such as deep learning, edge computing and IoT, smart health is playing vital role in healthcare industry to increase the accuracy, reliability, and productivity of mobile sensory devices.

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JBHI Journal 2022 Journal Article

Hybrid Intelligence-Driven Medical Image Recognition for Remote Patient Diagnosis in Internet of Medical Things

Zhiwei Guo
Yu Shen
Shaohua Wan
Wen-Long Shang
Keping Yu

In ear of smart cities, intelligent medical image recognition technique has become a promising way to solve remote patient diagnosis in IoMT. Although deep learning-based recognition approaches have received great development during the past decade, explainability always acts as a main obstacle to promote recognition approaches to higher levels. Because it is always hard to clearly grasp internal principles of deep learning models. In contrast, the conventional machine learning (CML)-based methods are well explainable, as they give relatively certain meanings to parameters. Motivated by the above view, this paper combines deep learning with the CML, and proposes a hybrid intelligence-driven medical image recognition framework in IoMT. On the one hand, the convolution neural network is utilized to extract deep and abstract features for initial images. On the other hand, the CML-based techniques are employed to reduce dimensions for extracted features and construct a strong classifier that output recognition results. A real dataset about pathologic myopia is selected to establish simulative scenario, in order to assess the proposed recognition framework. Results reveal that the proposal that improves recognition accuracy about two to three percent.

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JBHI Journal 2019 Journal Article

Knowledge-Aided Convolutional Neural Network for Small Organ Segmentation

Yu Zhao
Hongwei Li
Shaohua Wan
Anjany Sekuboyina
Xiaobin Hu
Giles Tetteh
Marie Piraud
Bjoern Menze

Accurate and automatic organ segmentation is critical for computer-aided analysis towards clinical decision support and treatment planning. State-of-the-art approaches have achieved remarkable segmentation accuracy on large organs, such as the liver and kidneys. However, most of these methods do not perform well on small organs, such as the pancreas, gallbladder, and adrenal glands, especially when lacking sufficient training data. This paper presents an automatic approach for small organ segmentation with limited training data using two cascaded steps- localization and segmentation. The localization stage involves the extraction of the region of interest after the registration of images to a common template and during the segmentation stage, a voxel-wise label map of the extracted region of interest is obtained and then transformed back to the original space. In the localization step, we propose to utilize a graph-based groupwise image registration method to build the template for registration so as to minimize the potential bias and avoid getting a fuzzy template. More importantly, a novel knowledge-aided convolutional neural network is proposed to improve segmentation accuracy in the second stage. This proposed network is flexible and can combine the effort of both deep learning and traditional methods, consequently achieving better segmentation relative to either of individual methods. The ISBI 2015 VISCERAL challenge dataset is used to evaluate the presented approach. Experimental results demonstrate that the proposed method outperforms cutting-edge deep learning approaches, traditional forest-based approaches, and multiatlas approaches in the segmentation of small organs.

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