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Hongwei Li

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19 papers

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AAAI Conference 2026 Conference Paper

ConfGuard: A Simple and Effective Backdoor Detection for Large Language Models

Zihan Wang
Rui Zhang
Hongwei Li
Wenshu Fan
Wenbo Jiang
Qingchuan Zhao
Guowen Xu

Backdoor attacks pose a significant threat to Large Language Models (LLMs), where adversaries can embed hidden triggers to manipulate LLM's outputs. Most existing defense methods, primarily designed for classification tasks, are ineffective against the autoregressive nature and vast output space of LLMs, thereby suffering from poor performance and high latency. To address these limitations, we investigate the behavioral discrepancies between benign and backdoored LLMs in output space. We identify a critical phenomenon which we term sequence lock: a backdoored model generates the target sequence with abnormally high and consistent confidence compared to benign generation. Building on this insight, we propose ConfGuard, a lightweight and effective detection method that monitors a sliding window of token confidences to identify sequence lock. Extensive experiments demonstrate ConfGuard achieves a near 100% true positive rate (TPR) and a negligible false positive rate (FPR) in the vast majority of cases. Crucially, the ConfGuard enables real-time detection almost without additional latency, making it a practical backdoor defense for real-world LLM deployments.

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AAAI Conference 2026 Conference Paper

Leveraging Visual Blur Perception Characteristics for EEG Decoding

Wenchao Liu
Hongwei Li
Zhouyang Xu
Lin Ma
Haifeng Li

In recent years, electroencephalography (EEG)-based visual decoding research has become a key direction for revealing brain processing mechanisms and realizing brain-computer interfaces. This emerging field has attracted extensive attention in the fields of brain science, cognitive neuroscience, and artificial intelligence. Among various approaches, contrastive learning has demonstrated strong performance in aligning multi-modal data, effectively enabling unified representations across modalities. However, during human visual perception, images are often subject to varying degrees of blurring due to the uneven distribution of retinal photoreceptor cells and the limited speed of lens accommodation. To address the mismatch between EEG and visual representations, we propose a novel visual decoding framework inspired by human perceptual blurring. Specifically, multi-level Gaussian blurring is applied to the visual stimuli to simulate human visual characteristics, followed by a feature selection module to construct robust visual representations. For EEG decoding, we design a lightweight and efficient network employing positively constrained spatial convolutions to identify channels associated with visual processing. The EEG and visual features are then aligned using contrastive learning. We evaluate the proposed framework on the Things-EEG dataset. Experimental results show significant improvements in the zero-shot brain-to-image retrieval task, achieving a top-1 accuracy of 80% and a top-5 accuracy of 96.9%, surpassing previous state-of-the-art methods by margins of 29.1% and 17.2%, respectively. These findings highlight the potential of incorporating perceptual properties into EEG-based visual decoding.

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AAAI Conference 2026 Conference Paper

MartDE: A Privacy-Preserving and Cost-Efficient Evaluation Framework for Data Marketplaces

Xinyuan Qian
Haoyong Wang
Hangcheng Cao
Shuai Yuan
Senkang Hu
Qingchuan Zhao
Hongwei Li
Guowen Xu

The development of machine learning models increasingly relies on high-quality data that resides in private domains. To enable secure and value-driven data exchange under strict privacy regulations, federated learning (FL) has emerged as a key primitive by enabling the trading of model utilities instead of raw data. Among existing solutions, martFL (CCS 2023) represents the state-of-the-art FL-based data marketplace architecture, integrating privacy-preserving model evaluation and verifiable trading protocols to enable robust and fair model utility trading without revealing raw data. Despite its strengths, martFL suffers from critical weaknesses at the evaluation layer, including plaintext score exposure and unverifiable and manipulable participant selection. To address these challenges, we propose MartDE, a dedicated evaluation framework that builds model-centric data marketplaces with robust, privacy-preserving, and verifiable mechanisms. MartDE introduces encrypted utility scoring with client-side decryption to preserve score confidentiality, formally bounded anomaly filtering, adaptive participant selection based on global model performance, and commitment-based verification to ensure consistency between declared and evaluated scores and selection verification. We implement MartDE and evaluate it across diverse datasets and adversarial conditions. Results show that MartDE achieves superior accuracy, robustness, and cost-efficiency, providing a strong foundation for secure and trustworthy utility-driven data marketplaces.

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AAAI Conference 2026 Conference Paper

MPMA: Preference Manipulation Attack Against Model Context Protocol

Zihan Wang
Rui Zhang
Yu Liu
Wenshu Fan
Wenbo Jiang
Qingchuan Zhao
Hongwei Li
Guowen Xu

Model Context Protocol (MCP) standardizes interface mapping for large language models (LLMs) to access external data and tools, which revolutionizes the paradigm of tool selection and facilitates the rapid expansion of the LLM agent tool ecosystem. However, as the MCP is increasingly adopted, third-party customized versions of the MCP server expose potential security vulnerabilities. In this paper, we first introduce a novel security threat, which we term the MCP Preference Manipulation Attack (MPMA). An attacker deploys a customized MCP server to manipulate LLMs, causing them to prioritize it over other competing MCP servers. This can result in economic benefits for attackers, such as revenue from paid MCP services or advertising income generated from free servers. To achieve MPMA, we first design a Direct Preference Manipulation Attack (DPMA) that achieves significant effectiveness by inserting the manipulative word and phrases into the tool name and description. However, such a direct modification is obvious to users and lacks stealthiness. To address these limitations, we further propose Genetic-based Advertising Preference Manipulation Attack (GAPMA). GAPMA employs four commonly used strategies to initialize descriptions and integrates a Genetic Algorithm (GA) to enhance stealthiness. The experiment results demonstrate that GAPMA balances high effectiveness and stealthiness. Our study reveals a critical vulnerability of the MCP in open ecosystems, highlighting an urgent need for robust defense mechanisms to ensure the fairness of the MCP ecosystem.

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EAAI Journal 2026 Journal Article

Near-point-of-care identification of mango fruit species via a cloud platform bridging smartphone and deep learning

Jiasheng Chen
Hongwei Li
Xindong Lai
Jiqing Chen
Junduan Huang
Zhenzhen Jin
Deqiang He

The reliance on physical labels and subjective visual inspection for mango tracking is prone to damage and inconsistency. To address the issue, this study develops a cloud-based system for accurate, on-tree mango species identification. A lightweight deep learning model based on an improved ShuffleNet architecture, integrating ECA(Efficient Channel Attention) and GC(Global Context) modules, enhances feature discrimination while maintaining efficiency. The improved model achieves identification precisions of 99. 48%, 96. 55%, 100. 00% and 97. 56% for Aomang, Guiqi, Jihuang and Tainong respectively. The overall identification accuracy reaches 98. 375%, with a parameter quantity of 1. 53 M(million) and FLOPs(Floating Point Operations per Second) of 2. 3 GMac(Giga Multiply-Accumulate Operations). It outperformed other models like DenseNet169 and ResNet34 by 2. 375% and 5. 375% in accuracy. The improved model is further developed into an Android application, Average processing image time for 100 test samples is less than 1550 ms, providing a near-point-of-care, non-destructive, and label-free solution for species identification. This application aids users in optimizing harvest planning and improving germplasm management, offering a practical solution for mobile and cloud-based deployment in precision agriculture.

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YNICL Journal 2025 Journal Article

A multi-modal study on cerebrovascular dysfunction in cognitive decline of de novo Parkinson’s disease

Hongwei Li
Xiali Shao
Jia Jia
Bingyi Wang
Jian Wang
Kai Liu
Jinhan Chen
Zhensen Chen

BACKGROUND: Vascular risk factors are increasingly implicated in Parkinson's disease (PD), but the role of altered cerebrovascular dysfunction in early-stage PD remains unclear. Here, we investigated resting-state cerebrovascular reactivity (RS-CVR), cerebral blood flow (CBF), arterial morphological changes, and corresponding alterations in functional connectivity density (FCD) in de novo PD patients with different cognitive status. METHODS: 25 de novo PD patients with mild cognitive impairment (PD-MCI), 34 with normal cognition (PD-NC), and 48 healthy controls (HCs) underwent neuropsychological assessments and multimodal MRI. CBF derived from arterial spin labeling, RS-CVR and FCD generated from resting-state functional MRI and the arterial morphology extracted from the magnitude images of multi-echo gradient echo. RESULTS: RS-CVR significantly decreased in PD patients, particularly in the left occipital gyrus and posterior cerebral artery (PCA) territories. Long-range FCD was reduced in the left inferior occipital gyrus in both PD-NC and PD-MCI compared to HCs (p = 0.005, p < 0.001). In PD-MCI, negative correlations between Stroop Color-Word Test time and RS-CVR in the distal right PCA (r = -0.71, pFDR = 0.030) and middle left PCA (r = -0.66, pFDR = 0.044) were observed. A significant correlation was found between decreased long-range FCD in the left inferior occipital gyrus and poorer Trail Making Test Part B performance (r = -0.63, pFDR = 0.029) in the PD-MCI. No significant differences in CBF, but significant dilation of the left PCA and compensatory CBF increases in the corresponding territory in PD-MCI were found (r = 0.57, pFDR = 0.023). DISCUSSION: Microvascular dysfunction, rather than perfusion defects, might underlie early-stage of the de novo PD, especially in the patients with PD-MCI.

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EAAI Journal 2025 Journal Article

A novel anomaly detection and classification algorithm for application in tuyere images of blast furnace

Yifan Duan
Xiaojie Liu
Ran Liu
Xin Li
Hongwei Li
Hongyang Li
Yanqin Sun
Yujie Zhang

Traditional relying on manual experience to assess the tuyere status consumes significant human resources. In the era of intelligent blast furnaces and intensified smelting, this approach struggles to meet the demands for accuracy and real-time assessment, posing challenges to safety and efficiency of blast furnace production. Tuyere images exhibit high feature similarity, and the number of samples is often limited. Therefore, if a simple convolution operation is only used, it will be difficult to discern differences across various images. To address this challenge and cater to the requirements of intelligent tuyere status recognition across different steel enterprises, we designed a novel deep neural network algorithm called ES-SFRNet (Enhanced Sequential: Feature Fusion and Recognition Network), building upon our prior research. The algorithm concurrently modeled tuyere images alongside relevant time series data, comprising three components: Feature pre-extraction, Tuyere status recognition, and Generalization & Robustness. The first two modules focus on feature extraction and fusion of tuyere images, while leveraging edge detection information from the image, we developed a mathematical index A r (Area Ratio) to serve as an auxiliary criterion for tuyere status recognition. Given the model's future scalability and multi-scenario application, the final module focuses on knowledge integration and parameter control. Test results reveal an overall accuracy rate of 99. 3% for the ES-SFRNet algorithm, effectively capturing key parameters to facilitate on-site operations. In comparison to other mainstream object detection algorithms, our algorithm framework excels in tuyere image feature extraction and recognition, which can offer broad applications to Chinese blast furnace ironmaking industry.

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EAAI Journal 2025 Journal Article

An effective hybrid algorithm with log-sum regularization and modified momentum for restricted Boltzmann machine

Huihui Shen
Hongwei Li
Zhiguo Gong
Qing Li

Restricted Boltzmann Machine (RBM) is a strong representation and generative model for unsupervised feature extraction in deep learning. Deep architectures can effectively enhance the capability for image feature expression in image recognition. However, deep architectures have two main disadvantages: 1) they are prone to overfitting, and 2) they are computationally expensive. We propose an effective hybrid algorithm with log-sum regularization and modified momentum for RBM, aiming to overcome overfitting and enhance computational efficiency. Firstly, we improve the gradient approximation by incorporating bias modification. Secondly, we incorporate the log-sum norm into RBM to achieve sparse representation, allowing the sparsity of each hidden unit to be learned adaptively. Thirdly, different combinations of the momentum term are used in the pre-training and fine-tuning phases of the RBM to accelerate the network convergence. Recognition experiments on the MNIST, CMU-PIE, and Extended Yale B databases demonstrate the advantages of our method over other deep learning algorithms in terms of recognition performance and computational efficiency. Denoising experiments for removing the random noise indicate good image denoising capabilities of our proposed method over other unsupervised learning algorithms in natural image data, simulated seismic data, and real seismic data. Based on these image recognition and image denoising results, this novel combination of the improved gradient approximation with modified momentum and the log-sum regularization for training RBM is an effective method for learning robust generative and representation models. Additionally, our research provides a reference for using RBMs to develop engineering applications of artificial intelligence.

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NeurIPS Conference 2025 Conference Paper

Co-PatcheR: Collaborative Software Patching with Component-specific Small Reasoning Models

Yuheng Tang
Hongwei Li
Kaijie Zhu
Michael Yang
Yangruibo Ding
Wenbo Guo

Motivated by the success of general‑purpose large language models (LLMs) in software patching, recent works started to train specialized patching models. Most works trained one model to handle the end‑to‑end patching pipeline (including issue localization, patch generation, and patch validation). However, it is hard for a small model to handle all tasks, as different sub-tasks have different workflows and require different expertise. As such, by using a 70 billion model, SOTA methods can only reach up to 41% resolved rate on SWE-bench-Verified. Motivated by the collaborative nature, we propose Co-PatcheR, the first collaborative patching system with small and specialized reasoning models for individual components. Our key technique novelties are the specific task designs and training recipes. First, we train a model for localization and patch generation. Our localization pinpoints the suspicious lines through a two-step procedure, and our generation combines patch generation and critique. We then propose a hybrid patch validation that includes two models for crafting issue-reproducing test cases with and without assertions and judging patch correctness, followed by a majority vote-based patch selection. Through extensive evaluation, we show that Co-PatcheR achieves 46% resolved rate on SWE-bench-Verified with only 3 x 14B models. This makes Co-PatcheR the best patcher with specialized models, requiring the least training resources and the smallest models. We conduct a comprehensive ablation study to validate our recipes, as well as our choice of training data number, model size, and testing-phase scaling strategy.

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YNIMG Journal 2025 Journal Article

DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling

Zongren Li
Shuping Luo
Hongwei Li
Yanbin Li

This study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational complexity that stem from the utilization of large convolutional kernels. Even though the size of the convolutional kernels has been extended up to 51×51, the enhancement of performance has hit a plateau, and moreover, striped convolution incurs a performance degradation. Enlightened by the hierarchical visual processing mechanism inherent in humans, this research innovatively incorporates a shared parameter mechanism for large convolutional kernels. It synergizes the expansion of the receptive field enabled by large convolutional kernels with the extraction of fine-grained features facilitated by small convolutional kernels. To address the surging number of parameters, a meticulously designed parameter sharing mechanism is employed, featuring fine-grained processing in the central region of the convolutional kernel and wide-ranging parameter sharing in the periphery. This not only curtails the parameter count and mitigates the model complexity but also sustains the model's capacity to capture extensive spatial relationships. Additionally, in light of the problems of spatial feature information loss and augmented memory access during the 1 × 1 convolutional channel compression phase, this study further puts forward a dynamic channel sampling approach, which markedly elevates the accuracy of tumor subregion segmentation. To authenticate the efficacy of the proposed methodology, a comprehensive evaluation has been conducted on three brain tumor segmentation datasets, namely BraTs2020, BraTs2024, and Medical Segmentation Decathlon Brain 2018. The experimental results evince that the proposed model surpasses the current mainstream ConvNet and Transformer architectures across all performance metrics, proffering novel research perspectives and technical stratagems for the realm of medical image segmentation.

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NeurIPS Conference 2025 Conference Paper

The Fluorescent Veil: A Stealthy and Effective Physical Adversarial Patch Against Traffic Sign Recognition

Shuai Yuan
Xingshuo Han
Hongwei Li
Guowen Xu
Wenbo Jiang
Tao Ni
Qingchuan Zhao
Yuguang Fang

Recently, traffic sign recognition (TSR) systems have become a prominent target for physical adversarial attacks. These attacks typically rely on conspicuous stickers and projections, or using invisible light and acoustic signals that can be easily blocked. In this paper, we introduce a novel attack medium, i. e. , fluorescent ink, to design a stealthy and effective physical adversarial patch, namely FIPatch, to advance the state-of-the-art. Specifically, we first model the fluorescence effect in the digital domain to identify the optimal attack settings, which guide the real-world fluorescence parameters. By applying a carefully designed fluorescence perturbation to the target sign, the attacker can later trigger a fluorescent effect using invisible ultraviolet light, causing the TSR system to misclassify the sign and potentially leading to traffic accidents. We conducted a comprehensive evaluation to investigate the effectiveness of FIPatch, which shows a success rate of 98. 31% in low-light conditions. Furthermore, our attack successfully bypasses five popular defenses and achieves a success rate of 96. 72%.

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AAAI Conference 2025 Conference Paper

Watch Out for Your Guidance on Generation! Exploring Conditional Backdoor Attacks against Large Language Models

Jiaming He
Wenbo Jiang
Guanyu Hou
Wenshu Fan
Rui Zhang
Hongwei Li

Mainstream backdoor attacks on large language models (LLMs) typically set a fixed trigger in the input instance and specific responses for triggered queries. However, the fixed trigger setting (e.g., unusual words) may be easily detected by human detection, limiting the effectiveness and practicality in real-world scenarios. To enhance the stealthiness of backdoor activation, we present a new poisoning paradigm against LLMs triggered by specifying generation conditions, which are commonly adopted strategies by users during model inference. The poisoned model performs normally for output under normal/other generation conditions, while becomes harmful for output under target generation conditions. To achieve this objective, we introduce BrieFool, an efficient attack framework. It leverages the characteristics of generation conditions by efficient instruction sampling and poisoning data generation, thereby influencing the behavior of LLMs under target conditions. Our attack can be generally divided into two types with different targets: Safety unalignment attack and Ability degradation attack. Our extensive experiments demonstrate that BrieFool is effective across safety domains and ability domains, achieving higher success rates than baseline methods, with 94.3% on GPT-3.5-turbo.

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NeurIPS Conference 2022 Conference Paper

Iron: Private Inference on Transformers

Meng Hao
Hongwei Li
Hanxiao Chen
Pengzhi Xing
Guowen Xu
Tianwei Zhang

We initiate the study of private inference on Transformer-based models in the client-server setting, where clients have private inputs and servers hold proprietary models. Our main contribution is to provide several new secure protocols for matrix multiplication and complex non-linear functions like Softmax, GELU activations, and LayerNorm, which are critical components of Transformers. Specifically, we first propose a customized homomorphic encryption-based protocol for matrix multiplication that crucially relies on a novel compact packing technique. This design achieves $\sqrt{m} \times$ less communication ($m$ is the number of rows of the output matrix) over the most efficient work. Second, we design efficient protocols for three non-linear functions via integrating advanced underlying protocols and specialized optimizations. Compared to the state-of-the-art protocols, our recipes reduce about half of the communication and computation overhead. Furthermore, all protocols are numerically precise, which preserve the model accuracy of plaintext. These techniques together allow us to implement \Name, an efficient Transformer-based private inference framework. Experiments conducted on several real-world datasets and models demonstrate that \Name achieves $3 \sim 14\times$ less communication and $3 \sim 11\times$ less runtime compared to the prior art.

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AAAI Conference 2021 Conference Paper

A Bottom-Up DAG Structure Extraction Model for Math Word Problems

Yixuan Cao
Feng Hong
Hongwei Li
Ping Luo

Research on automatically solving mathematical word problems (MWP) has a long history. Most recent works adopt the Seq2Seq approach to predict the result equations as a sequence of quantities and operators. Although result equations can be written as a sequence, it is essentially a structure. More precisely, it is a Direct Acyclic Graph (DAG) whose leaf nodes are the quantities, and internal and root nodes are arithmetic or comparison operators. In this paper, we propose a novel Seq2DAG approach to extract the equation set directly as a DAG structure. It extracts the structure in a bottom-up fashion by aggregating quantities and sub-expressions layer by layer iteratively. The advantages of our approach are threefold: it is intrinsically suitable to solve multivariate problems, it always outputs valid structure, and its computation satisfies commutative law for +, × and =. Experimental results on DRAW1K and Math23K datasets demonstrate that our model outperforms state-of-the-art deep learning methods. We also conduct detailed analysis on the results to show the strengths and limitations of our approach.

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YNIMG Journal 2021 Journal Article

Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: The ADAM challenge

Kimberley M. Timmins
Irene C. van der Schaaf
Edwin Bennink
Ynte M. Ruigrok
Xingle An
Michael Baumgartner
Pascal Bourdon
Riccardo De Feo

Accurate detection and quantification of unruptured intracranial aneurysms (UIAs) is important for rupture risk assessment and to allow an informed treatment decision to be made. Currently, 2D manual measures used to assess UIAs on Time-of-Flight magnetic resonance angiographies (TOF-MRAs) lack 3D information and there is substantial inter-observer variability for both aneurysm detection and assessment of aneurysm size and growth. 3D measures could be helpful to improve aneurysm detection and quantification but are time-consuming and would therefore benefit from a reliable automatic UIA detection and segmentation method. The Aneurysm Detection and segMentation (ADAM) challenge was organised in which methods for automatic UIA detection and segmentation were developed and submitted to be evaluated on a diverse clinical TOF-MRA dataset. A training set (113 cases with a total of 129 UIAs) was released, each case including a TOF-MRA, a structural MR image (T1, T2 or FLAIR), annotation of any present UIA(s) and the centre voxel of the UIA(s). A test set of 141 cases (with 153 UIAs) was used for evaluation. Two tasks were proposed: (1) detection and (2) segmentation of UIAs on TOF-MRAs. Teams developed and submitted containerised methods to be evaluated on the test set. Task 1 was evaluated using metrics of sensitivity and false positive count. Task 2 was evaluated using dice similarity coefficient, modified hausdorff distance (95th percentile) and volumetric similarity. For each task, a ranking was made based on the average of the metrics. In total, eleven teams participated in task 1 and nine of those teams participated in task 2. Task 1 was won by a method specifically designed for the detection task (i. e. not participating in task 2). Based on segmentation metrics, the top two methods for task 2 performed statistically significantly better than all other methods. The detection performance of the top-ranking methods was comparable to visual inspection for larger aneurysms. Segmentation performance of the top ranking method, after selection of true UIAs, was similar to interobserver performance. The ADAM challenge remains open for future submissions and improved submissions, with a live leaderboard to provide benchmarking for method developments at https: //adam. isi. uu. nl/.

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JBHI Journal 2020 Journal Article

Coarse-to-Fine Adversarial Networks and Zone-Based Uncertainty Analysis for NK/T-Cell Lymphoma Segmentation in CT/PET Images

Xiaobin Hu
Rui Guo
Jieneng Chen
Hongwei Li
Diana Waldmannstetter
Yu Zhao
Biao Li
Kuangyu Shi

Extranodal natural killer/T cell lymphoma (ENKL), nasal type is a kind of rare disease with a low survival rate that primarily affects Asian and South American populations. Segmentation of ENKL lesions is crucial for clinical decision support and treatment planning. This paper is the first study on computer-aided diagnosis systems for the ENKL segmentation problem. We propose an automatic, coarse-to-fine approach for ENKL segmentation using adversarial networks. In the coarse stage, we extract the region of interest bounding the lesions utilizing a segmentation neural network. In the fine stage, we use an adversarial segmentation network and further introduce a multi-scale L 1 loss function to drive the network to learn both global and local features. The generator and discriminator are alternately trained by backpropagation in an adversarial fashion in a min-max game. Furthermore, we present the first exploration of zone-based uncertainty estimates based on Monte Carlo dropout technique in the context of deep networks for medical image segmentation. Specifically, we propose the uncertainty criteria based on the lesion and the background, and then linearly normalize them to a specific interval. This is not only the crucial criterion for evaluating the superiority of the algorithm, but also permits subsequent optimization by engineers and revision by clinicians after quantitatively understanding the main source of uncertainty from the background or the lesion zone. Experimental results demonstrate that the proposed method is more effective and lesion-zone stable than state-of-the-art deep-learning based segmentation model.

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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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TIST Journal 2019 Journal Article

Large-Scale Frequent Episode Mining from Complex Event Sequences with Hierarchies

Xiang Ao
Haoran Shi
Jin Wang
Luo Zuo
Hongwei Li
Qing He

Frequent Episode Mining (FEM), which aims at mining frequent sub-sequences from a single long event sequence, is one of the essential building blocks for the sequence mining research field. Existing studies about FEM suffer from unsatisfied scalability when faced with complex sequences as it is an NP-complete problem for testing whether an episode occurs in a sequence. In this article, we propose a scalable, distributed framework to support FEM on “big” event sequences. As a rule of thumb, “big” illustrates an event sequence is either very long or with masses of simultaneous events. Meanwhile, the events in this article are arranged in a predefined hierarchy. It derives some abstractive events that can form episodes that may not directly appear in the input sequence. Specifically, we devise an event-centered and hierarchy-aware partitioning strategy to allocate events from different levels of the hierarchy into local processes. We then present an efficient special-purpose algorithm to improve the local mining performance. We also extend our framework to support maximal and closed episode mining in the context of event hierarchy, and to the best of our knowledge, we are the first attempt to define and discover hierarchy-aware maximal and closed episodes. We implement the proposed framework on Apache Spark and conduct experiments on both synthetic and real-world datasets. Experimental results demonstrate the efficiency and scalability of the proposed approach and show that we can find practical patterns when taking event hierarchies into account.

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YNIMG Journal 2018 Journal Article

Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images

Hongwei Li
Gongfa Jiang
Jianguo Zhang
Ruixuan Wang
Zhaolei Wang
Wei-Shi Zheng
Bjoern Menze

White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then independently tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score, precision and robust Hausdorff distance obtained on held-out test datasets were 80%, 84% and 6. 30 mm respectively. These were the highest achieved in the challenge, suggesting the proposed method is the state-of-the-art. Detailed descriptions and quantitative analysis on key components of the system were provided. Furthermore, a study of cross-scanner evaluation is presented to discuss how the combination of modalities affect the generalization capability of the system. The adaptability of the system to different scanners and protocols is also investigated. A quantitative study is further presented to show the effect of ensemble size and the effectiveness of the ensemble model. Additionally, software and models of our method are made publicly available. The effectiveness and generalization capability of the proposed system show its potential for real-world clinical practice.

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