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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jiquan Deng, Chengdong Chen, Geng Jiang, Haotian Huang, Shijie Kim, Jaeil Wen, Xuyun Shen, Dinggang Heilongjiang University Department of Computer Science and Technology Harbin China Northwestern Polytechnical University School of Computer Science and Engineering Xi'an China Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Nanjing University of Aeronautics and Astronautics School of Computer Science and Technology Nanjing China ShanghaiTech University School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai China Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Shanghai Clinical Research and Trial Center Shanghai China

ISBN: (纸本)9798350386226

Diffusion magnetic resonance imaging (dMRI), as a powerful non-invasive white matter imaging technology, plays an important role in studying brain white matter. The fiber orientation distribution functions (fODFs) derived from dMRI data provide the key directional information of fiber tracts for revealing the 3D geometric structure of brain white matter. The estimation of fODFs faces two challenges, including (i) the demand for dMRI data densely sampled in q-space and (ii) the joint consideration of x-q space. To address these challenges, we propose a mixture learning framework with q-space sparely sampled dMRI data as input. Specifically, we propose an x-space learning module based on 3D U-Net to learn x-space features and a q-space learning module based on spherical convolutional neural networks to learn q-space features. Two kinds of features are then fused with a mixture learning fusion module for fODFs estimation. The whole framework is supervised with an x-q space loss function. Our framework makes full use of joint x-q space information for fODFs estimation with clinically available q-space sparsely sampled dMRI data. Extensive experiments on three public datasets show that our framework is effective in fODFs estimation and outperforms cutting-edge models. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Development of machine learning-based models to predict 10-year risk of cardiovascular disease:a prospective cohort study

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Stroke & Vascular Neurology 2023年第6期8卷 475-485页

作者： Jia You Yu Guo Ju-Jiao Kang Hui-Fu Wang Ming Yang Jian-Feng Feng Jin-Tai Yu Wei Cheng Institute of Science and Technology for Brain-Inspired Intelligence Department of NeurologyHuashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan UniversityMinistry of EducationShanghaiChina Zhangjiang Fudan International Innovation Center Fudan UniversityShanghaiChina School of Data Science Fudan UniversityShanghaiChina Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence Zhejiang Normal UniversityZhejiangChina Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center Fudan UniversityShanghaiChina

Background Previous prediction algorithms for cardiovascular diseases(CVD)were established using risk factors retrieved largely based on empirical clinical *** study sought to identify predictors among a comprehensive variable space,and then employ machine learning(ML)algorithms to develop a novel CVD risk prediction *** From a longitudinal population-based cohort of UK Biobank,this study included 473611 CVD-free participants aged between 37 and 73 years *** implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident *** model was validated through a leave-one center-out *** During a median follow-up of 12.2 years,31466 participants developed CVD within 10 years after baseline visits.A novel UK Biobank CVD risk prediction(UKCRP)model was established that comprised 10 predictors including age,sex,medication of cholesterol and blood pressure,cholesterol ratio(total/high-density lipoprotein),systolic blood pressure,previous angina or heart disease,number of medications taken,cystatin C,chest pain and pack-years of *** model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve(AUC)of 0.762±0.010 that outperformed multiple existing clinical models,and it was well-calibrated with a Brier Score of 0.057±***,the UKCRP can obtain comparable performance for myocardial infarction(AUC 0.774±0.011)and ischaemic stroke(AUC 0.730±0.020),but inferior performance for haemorrhagic stroke(AUC 0.644±0.026).Conclusion ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.

关键词： cardiovascular prediction learning

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Multi-Metric Based Client Selection for Backdoor Defense in Federated Learning 1

Multi-Metric Based Client Selection for Backdoor Defense in ...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Yuan, Changjiang Li, Yidong Chen, Naiyue Zhang, Zikai Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781643685779

The distributed nature of federated learning makes it highly susceptible to backdoor attacks, which aim to induce the model to produce incorrect results when specific data is input. Existing defense methods based on distance calculations mostly use one single distance, which can be easily circumvented by backdoor attacks. Therefore, we conducted research on the combination of multiple distances. Based on this, we propose a backdoor defense algorithm based on multi metrics, which evaluates models through distance calculation and normalization to select benign models for aggregation. To verify the effectiveness of our method, we conducted experiments on three datasets, and the results prove that our method can provide good defense without reducing the model's main task accuracy. © 2025 The Authors.

关键词： Federated learning

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Towards Label-Efficient Deep Learning for Myopic Maculopathy Classification 1

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Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2023

作者： Hou, Junlin Xu, Jilan Xiao, Fan Zhang, Bo Xu, Yiqian Zhang, Yuejie Zou, Haidong Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopic Maculopathy is the leading cause of legal blindness in patients with pathologic myopia. Automated myopic maculopathy diagnosis is of vital importance to early treatment and progression slowdown. However, the scarcity of labeled fundus images with myopic maculopathy makes it challenging to improve diagnostic performance via deep learning models. In this paper, we construct a label-efficient deep learning framework for myopic maculopathy classification. In specific, we exploit two categories of pre-training methods, i.e., vision-language pre-training and self-supervised visual representation learning, to alleviate the overfitting problem caused by the limited number of training images. Moreover, we adopt a semi-supervised learning technique, namely pseudo labeling, to leverage a large number of unlabeled fundus images from external datasets. We also investigate the impact of other key components in model training for better performance, including backbone architecture, input resolution, and loss function. Our method is evaluated in the MICCAI 2023 Myopic Maculopathy Analysis Challenge (MMAC). Among 17 participating teams, our ensembled model ranked 1st on the leaderboard with an average score of 0.8752. The code will be publicly available at https://***/FDU-VTS/MMAC. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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Injective coloring of edge corona product of graphs 8

Injective coloring of edge corona product of graphs

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2024 8th International Conference on Mechanics, Mathematics and Applied Physics, ICMMAP 2024

作者： Zhao, Wenying Tian, Shuangliang School of Mathematics and Computer Science Northwest for Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Applications Northwest for Minzu University Lanzhou730030 China

The injective chromatic number χi(G) of a graph G is the minimum number of colors needed to color the vertices of G such that different vertices with the same neighbor are colored differently. In this here, we get some exact values of the injective chromatic number of the edge corona product of some graphs. © Published under licence by IOP Publishing Ltd.

关键词： Graph theory

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Group Buying Recommendation Model Based on Multi-task Learning

Group Buying Recommendation Model Based on Multi-task Learni...

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International Conference on data Engineering

作者： Shuoyao Zhai Baichuan Liu Deqing Yang Yanghua Xiao School of Data Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China School of Computer Science Fudan University Shanghai China

In recent years, group buying has become one popular kind of online shopping activities, thanks to its larger sales and lower unit price. Unfortunately, seldom research focuses on the recommendations specifically for group buying by now. Although some recommendation models have been proposed for group recommendation, they can not be directly used to achieve the real-world group buying recommendation, due to the essential difference between group recommendation and group buying recommendation. In this paper, we first formalize the task of group buying recommendation into two sub-tasks. Then, based on our insights into the correlations and interactions between the two sub-tasks, we propose a novel recommendation model for group buying, namely MGBR, which is built mainly with a multi-task learning module. To improve recommendation performance further, we devise some collaborative expert networks and adjusted gates in the multi-task learning module, to promote the information interaction between the two sub-tasks. Furthermore, we propose two auxiliary losses corresponding to the two sub-tasks, to refine the representation learning in our model. Our extensive experiments not only demonstrate that the augmented representations learned in our model result in better performance than previous recommendation models, but also justify the impacts of the specially designed components in our model. To reproduce our model’s recommendation results conveniently, we have provided our model’s source code and dataset on https://***/DeqingYang/MGBR.

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Learned distributed image compression with decoder side information

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Digital Communications and Networks 2025年第2期11卷 349-358页

作者： Yankai Yin Zhe Sun Peiying Ruan Ruidong Li Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai UniversityTianjin 300350China Institute of Natural Sciences Kanazawa UniversityIshikawa 9201164Japan Faculty of Health Data Science and Graduate School of Medicine Juntendo UniversityChiba 2790013Japan NVIDIA AI Technology Center NVIDIA JapanTokyo 1070052Japan

With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data ***-view image compression aims to improve compression efficiency by leveraging correlations between ***,the requirement of synchronization and inter-image communication at the encoder side poses significant challenges,especially for constrained *** this study,we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during *** model integrates an encoder network,a quantization module,and a decoder network,to ensure both high compression performance and high-quality image *** encoder uses a deep Convolutional Neural Network(CNN)to extract high-level features from the input image,which then pass through the quantization module for further compression before undergoing lossless entropy *** decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder ***,we first introduce a channel-spatial attention module to capture and refine information within individual image feature ***,we employ a semi-coupled convolution module to extract both shared and specific information in ***,a cross-attention module is employed to fuse mutual information extracted from side *** effectiveness of our model is validated on various datasets,including KITTI Stereo and *** results highlight the superior compression capabilities of our method,surpassing state-of-the-art techniques.

关键词： Digital communication Image compression Side information Channel-spatial attention module Cross-attention module

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ViolationTracker: Building Precise Histories for Static Analysis Violations

ViolationTracker: Building Precise Histories for Static Anal...

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International Conference on Software Engineering (ICSE)

作者： Ping Yu Yijian Wu Xin Peng Jiahan Peng Jian Zhang Peicheng Xie Wenyun Zhao School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China

Automatic static analysis tools (ASATs) detect source code violations to static analysis rules and are usually used as a guard for source code quality. The adoption of ASATs, however, is often challenged because of several problems such as a large number of false alarms, invalid rule priorities, and inappropriate rule configurations. Research has shown that tracking the history of the violations is a promising way to solve the above problems because the facts of violation fixing may reflect the developers' subjective expectations on the violation detection results. Precisely identifying the revisions that induce or fix a violation is however challenging because of the imprecise matching of violations between code revisions and ignorance of merge commits in the maintenance history. In this paper, we propose ViolationTracker, an approach to precisely matching the violation instances between adjacent revisions and building the life cycle of violations with the identification of inducing, fixing, deleting, and reopening of each violation case. The approach employs code entity anchoring heuristics for violation matching and considers merge commits that used to be ignored in existing research. We evaluate ViolationTracker with a manually-validated dataset that consists of 500 violation instances and 158 threads of 30 violation cases with detailed evolution history from open-source projects. Violation Tracker achieves over 93 % precision and 98 % recall on violation matching, outperforming the state-of-the-art approach, and 99.4 % precision on rebuilding the histories of violation cases. We also show that ViolationTracker is useful to identify actionable violations. A preliminary empirical study reveals the possibility to prioritize static analysis rules according to further analysis on the actionable rates of the rules.

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Learning to Unlearn for Bayesian Personalized Ranking via Influence Function

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Chinese Journal of Electronics 2025年第3期34卷 990-1001页

作者： Chen Jundong Zhang Honglei Li Haoxuan Li Yidong Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University) Ministry of Education Beijing 100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing 100044 China School of Computer Science and Technology Beijing Jiaotong University Beijing 100044 China Center for Data Science Peking University Beijing 100871 China

Learning recommender models from vast amounts of behavioral data has become a mainstream paradigm in recent information systems. Conversely, with privacy awareness grown, there has been increasing attention to the removal of sensitive or outlier data from well-trained recommendation models (known as recommendation unlearning). However, current unlearning methods primarily focus on fully/partially retraining the entire model. Despite considerable performance, it inevitably introduces significant efficiency bottlenecks, which is impractical for latency-sensitive streaming services. While recent efforts exploit efficient unlearning in point-wise recommender tasks, these approaches overlook the partial order relationships between items, resulting in suboptimal performance in both recommendation and unlearning capabilities. In light of this, we explore learning to unlearn for Bayesian personalized ranking via influence function, which relies on a pair-wise ranking loss to model user preferences and item characteristics, making unlearning more challenging than in point-wise settings. Specifically, we propose an influence function-guided unlearning framework tailored for pair-wise ranking models to efficiently perform unlearning requests, which involves unlearning partial order relationships while handling negative samples appropriately during the unlearning process. Besides, we prove that our proposed method can theoretically match the performance of retraining counterparts. Finally, we conduct extensive experiments to validate the effectiveness and efficiency of our model.

关键词： Recommendation unlearning Influence function Bayesian personalized ranking The right to be forgotten

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