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A deep learning system for predicting time to progression of diabetic retinopathy

NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning

arXiv

引用

arXiv 2023年

作者： Zhang, Xiaoqing Xiao, Zunjie Wu, Xiao Chen, Yanlin Zhao, Jilu Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore

Spatial attention mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysis. Unfortunately, existing efforts are often unaware that long-range dependency modeling has limitations in highlighting subtle lesion regions. To overcome this limitation, we propose a practical yet lightweight architectural unit, Pyramid Pixel Context Adaption (PPCA) module, which exploits multiscale pixel context information to recalibrate pixel position in a pixel-independent manner dynamically. PPCA first applies a well-designed cross-channel pyramid pooling to aggregate multiscale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization, and finally estimates per pixel attention weight via a pixel context integration. By embedding PPCA into a DNN with negligible overhead, the PPCANet is developed for medical image classification. In addition, we introduce supervised contrastive learning to enhance feature representation by exploiting the potential of label information via supervised contrastive loss. The extensive experiments on six medical image datasets show that PPCANet outperforms state-of-the-art attention-based networks and recent deep neural networks. We also provide visual analysis and ablation study to explain the behavior of PPCANet in the decision-making process. © 2023, CC BY.

关键词： Pixels

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

引用

arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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GESH-NET: GRAPH-ENHANCED SPHERICAL HARMONIC CONVOLUTIONAL NETWORKS FOR CORTICAL SURFACE REGISTRATION

arXiv

引用

arXiv 2024年

作者： Zhang, Ruoyu Wang, Lihui Tang, Kun Xu, Jingwen Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Cortical surface registration plays a crucial role in coordinating individual cortical functions and anatomical features, serving as a fundamental step in cortical surface analysis. Its aim is to align the anatomical or functional regions of different individuals, which is of great importance for neuroimaging studies across different populations. Currently, cortical surface registration techniques based on classical methods have been well developed. However, a key issue with classical registration methods is that for each pair of images to be registered, it is necessary to search for the optimal transformation in the deformation space according to a specific optimization algorithm until the similarity measure function converges, which cannot meet the requirements of real-time and high-precision in medical image registration. With the spectacular success of deep learning in the field of computer vision, researching cortical surface image registration techniques based on deep learning models has become a new direction. But so far, there are still only a few studies on cortical surface image registration based on deep learning. Moreover, although deep learning methods theoretically have stronger representation capabilities, surpassing the most advanced classical methods in registration accuracy and distortion control remains a challenge. Therefore, to address this challenge, this paper constructs a deep learning model to study the technology of cortical surface image registration. The specific work is as follows: (1) An unsupervised cortical surface registration network based on a multi-scale cascaded structure is designed, and a convolution method based on spherical harmonic transformation is introduced to register cortical surface data. This solves the problem of scale-inflexibility of spherical feature transformation and optimizes the multi-scale registration process. The results show that the proposed network outperforms the other deep learning-based registration m

关键词： Spheres

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Cluster-guided Contrastive Class-imbalanced Graph Classification 39

Cluster-guided Contrastive Class-imbalanced Graph Classifica...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ju, Wei Mao, Zhengyang Yi, Siyu Qin, Yifang Gu, Yiyang Xiao, Zhiping Shen, Jianhao Qiao, Ziyue Zhang, Ming College of Computer Science Sichuan University Chengdu China School of Computer Science State Key Laboratory for Multimedia Information Processing PKU-Anker LLM Lab Peking University Beijing China College of Mathematics Sichuan University Chengdu China Paul G. Allen School of Computer Science and Engineering University of Washington Seattle WA United States Huawei Hisilicon Shanghai China School of Computing and Information Technology Great Bay University Dongguan China

ISBN: (纸本)157735897X

This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions. While graph neural networks (GNNs) have achieved remarkable success, their modeling ability on imbalanced graph-structured data remains suboptimal, which typically leads to predictions biased towards the majority classes. On the other hand, existing class-imbalanced learning methods in vision may overlook the rich graph semantic substructures of the majority classes and excessively emphasize learning from the minority classes. To address these challenges, we propose a simple yet powerful approach called C³GNN that integrates the idea of clustering into contrastive learning to enhance class-imbalanced graph classification. Technically, C³GNN clusters graphs from each majority class into multiple subclasses, with sizes comparable to the minority class, mitigating class imbalance. It also employs the Mixup technique to generate synthetic samples, enriching the semantic diversity of each subclass. Furthermore, supervised contrastive learning is used to hierarchically learn effective graph representations, enabling the model to thoroughly explore semantic substructures in majority classes while avoiding excessive focus on minority classes. Extensive experiments on real-world graph benchmark datasets verify the superior performance of our proposed method against competitive baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Optimization of performance of the KM2A full array using the Crab Nebula

引用

Chinese Physics C 2024年第6期48卷 169-178页

作者：曹臻 F.Aharonian 安琪阿西克古白云翔包逸炜 D.Bastieri 毕效军毕玉江蔡金庭曹晴曹文羽曹喆常进常劲帆陈尚明陈恩生陈亮陈林陈龙陈明君陈玛丽陈起辉陈素弘陈天禄陈阳程宁程耀东崔明阳崔树旺崔晓红崔昱东戴本忠代洪亮戴子高单增罗布 D.della Volpe 董绪强段凯凯樊军辉范一中方军方堃冯存峰封莉冯少辉丰晓婷冯有亮 S.Gabici 高博高川东高林青高启高卫高伟康葛茂茂耿利斯 G.Giacinti 龚光华苟全补顾旻皓郭福来郭晓磊郭义庆郭莹莹韩毅昂何会海贺昊宁何佳银何新波何钰 M.Heller 贺远强侯博文侯超侯贤胡红波胡铨胡世聪黄代绘黄天奇黄文俊黄性涛黄晓渊黄勇黄志成季筱璐贾焕玉贾康江琨姜晓巍姜泽军金敏康明铭柯通 D.Kuleshov.K.Kurinov 李兵兵李澄李骢李丹李飞李海波李会财李华阳李军李剑李捷李凯李文龙李文莲李秀荣李昕李一卓李哲黎卓梁恩维梁云峰林苏杰刘冰刘成刘栋刘虎刘海东刘佳刘江来刘金艳刘茂元柳若愚刘四明刘伟刘怡刘以农鲁睿罗晴吕洪魁马伯强马玲玲马欣华毛基荣闵振 W.Mitthumsiri 穆慧君南云程 A.Neronov 区子维庞彬宇 P.Pattarakijwanich 裴致远齐孟尧祁业情乔冰强秦家军 D.Ruffolo A.Sáiz D.Semikoz 邵澄宇邵琅 O.Shchegolev 盛祥东舒富文宋慧超 Yu.V.Stenkin V.Stepanov 苏扬孙秦宁孙晓娜孙志斌谭柏轩唐庆文唐泽波田文武王超王昌贝王广威王洪光王惠惠王建成汪凯王利苹王玲玉王培汉王冉王为王祥高王祥玉王阳王玉东王岩谨王忠海王仲翔王振王铮韦大明魏俊杰魏永健文韬吴超勇吴含荣武莎吴雪峰吴雨生席邵强夏捷夏君集项光漫肖迪泫肖刚辛广广辛玉良邢祎熊峥徐东莲徐仁峰徐仁新徐伟立薛良闫大海颜景志颜田杨朝文杨帆杨冯帆杨何文杨佳盈杨莉莉杨明洁杨睿智杨深邦姚玉华姚志国叶一锰尹丽巧尹娜游晓浩游智勇于艳红袁强岳华曾厚敦曾婷轩曾玮查敏张彬彬张丰张海明张恒英张建立张丽霞张力张鹏飞张佩佩张瑞张少博张少如张寿山张潇张笑鹏张云峰张毅张勇赵兵赵静赵雷赵立志赵世平郑福周斌周浩周佳能周猛周平周荣周勋秀祝成光祝凤荣朱辉朱科军左雄 LHAASO Collaboration Key Laboratory of Particle Astrophyics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China Tianfu Cosmic Ray Research Center Chengdu 610000China Dublin Institute for Advanced Studies 31 Fitzwilliam Place2 DublinIreland Max-Planck-Institut for Nuclear Physics P.O.Box 10398069029 HeidelbergGermany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Hefei 230026China School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031China School of Astronomy and Space Science Nanjing UniversityNanjing 210023China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China Hebei Normal University Shijiazhuang 050024China Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of Radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000TibetChina National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Physics and Astronomy(Zhuhai)&School of Physics(Guangzhou)&Sino-French Institute of Nuclear Engineering and Technology(Zhuhai) Sun Yat-sen UniversityZhuhai 519000&Guangzhou 510275China School of Physics and Astronomy Yunnan UniversityKunming 650091China Département de Physique Nucléaire et Corpusculaire Facultéde SciencesUniversitéde Genève24 Quai Ernest Ansermet1211 GenevaSwitzerland Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 266237China APC Universit'e Paris Cit'eCNRS/IN2P3CEA/IRFUObservat

The full array of the Large high Altitude Air Shower Observatory(LHAASO)has been in operation since July *** its kilometer-square array(KM2A),we optimized the selection criteria for very high and ultrahigh energyγ-rays using data collected from August 2021 to August 2022,resulting in an improvement in significance of the detection in the Crab Nebula of approximately 15%,compared with that of previous *** the implementation of these new selection criteria,the angular resolution was also significantly improved by approximately 10%at tens of *** aspects of the full KM2A array performance,such as the pointing error,were also calibrated using the Crab *** resulting energy spectrum of the Crab Nebula in the energy range of 10-1000 TeV are well fitted by a log-parabola model,which is consistent with the previous results from LHAASO and other experiments.

关键词： γ-ray Crab Nebula significance

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OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Ni, Liwei Wang, Rui Liu, Miao Meng, Xingyu Lin, Xiaoze Liu, Junfeng Luo, Guojie Chu, Zhufei Qian, Weikang Yang, Xiaoyan Xie, Biwei Li, Xingquan Li, Huawei Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Pengcheng Laboratory Shenzhen518055 China University of Chinese Academy of Sciences Beijing101408 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University School of Computer Science Center for Energy-Efficient Computing and Applications Beijing100871 China Ninbo University Faculty of Electrical Engineering and Computer Science Ninbo315211 China Shanghai Jiao Tong University University of Michigan-Shanghai Jiao Tong University Joint Institute MoE Key Laboratory of Artificial Intelligence Shanghai200240 China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou311121 China

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated ML capabilities. While OpenLS-DGF supports various machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in both Verilog and ML-friendly GraphML formats. The Verilog files offer semi-customizable capabilities, enabling researchers to insert additional steps and incrementally refine the generated dataset. Furthermore, OpenLS-DGF includes an adaptive circuit engine that facilitates the final dataset management and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. OpenLS-D-v1 supports integrating new data features, making it more versatile for new tasks. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task is chosen to represent essential steps of logic synthesis, and the experimental results show the generated dataset from OpenLSDGF achieves prominent diversity and applicability. The source code and datasets are available at https://***/Logic-Factory/ACE/blob/master/OpenLS-DGF. © 1982-2012 IEEE.

关键词： Information management

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Eosinophil Detection with Modified YOLOv3 Model in Large Pathology Image

Eosinophil Detection with Modified YOLOv3 Model in Large Pat...

引用

computer Information science and Artificial Intelligence (CISAI), International Conference on

作者： Xinyue Wang Yonggang Che Nan Jiang Weijian Chen Long Lan State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China Hunan Children's Hospital Changsha China

ISBN: (纸本)9781665406932

Eosinophil detection and counting have an important role in the treatment of eosinophilic gastroenteritis. Generally, the physicians diagnose the eosinophilic gastroenteritis depending on the number of eosinophil cells in the corresponding pathology images. However, manually identifying eosinophils in the large pathology image is time-consuming and boring. The accuracy of Eosinophil counting significantly relies on the experiences of professional pathologists, the manual counting also prone to missing and misclassification due to fatigue. In this paper, we introduce a modified YOLOv3 object detection method to automatically count the eosinophil. Specifically, to detect the small and crowded eosinophils in the large pathology image, we carefully redesign the scale and size of candidate boxes and make our kernels more sensitive to tiny eosinophils in the feature maps. To verify our method, we collect and label a real eosinophil dataset with totally 8469 eosinophils being annotated and distributing over 255 pathology images from 11 different classes. We implemented our proposed method on our dataset and demonstrated the feasibility and bright prospect for applying object detection technique into the treatments of eosinophilic gastroenteritis.

关键词： Pathology Solid modeling Information science Image recognition Object detection Manuals Medical services

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Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos

arXiv

引用

arXiv 2023年

作者： Lin, Junhao Dai, Qian Zhu, Lei Fu, Huazhu Wang, Qiong Li, Weibin Rao, Wenhao Huang, Xiaoyang Wang, Liansheng School of Informatics Xiamen University Xiamen China Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Medicine Xiamen University Xiamen China

Breast lesion segmentation in ultrasound (US) videos is essential for diagnosing and treating axillary lymph node metastasis. However, the lack of a well-established and large-scale ultrasound video dataset with high-quality annotations has posed a persistent challenge for the research community. To overcome this issue, we meticulously curated a US video breast lesion segmentation dataset comprising 572 videos and 34,300 annotated frames, covering a wide range of realistic clinical scenarios. Furthermore, we propose a novel frequency and localization feature aggregation network (FLA-Net) that learns temporal features from the frequency domain and predicts additional lesion location positions to assist with breast lesion segmentation. We also devise a localization-based contrastive loss to reduce the lesion location distance between neighboring video frames within the same video and enlarge the location distances between frames from different ultrasound videos. Our experiments on our annotated dataset and two public video polyp segmentation datasets demonstrate that our proposed FLA-Net achieves state-of-the-art performance in breast lesion segmentation in US videos and video polyp segmentation while significantly reducing time and space complexity. Our model and dataset are available at https://***/jhlDet/FLA-Net. Copyright © 2023, The Authors. All rights reserved.

关键词： Location

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TailCmp - A Tail Latency Evaluation Solution of Public Cloud and Labeled von Neumann Architecture based Cloud Prototype

TailCmp - A Tail Latency Evaluation Solution of Public Cloud...

引用

IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Xiangyu Kong Xuesong Gao Shibao Pan Yizhi Zhou Yanan Yang Laiping Zhao Heng Qi School of Computer Science and Technology Dalian University of Technology Dalian China College of Intelligence and Computing Tianjin University Tianjin China State-Key Laboratory of Digital Multimedia Technology Qingdao China

Tail latency is the key performance metric for cloud computing systems (CCSs). When latency-critical (LC) applications and best-effort batch (BE) jobs are co-located on CCSs, the unmanaged contention for computing resources usually leads to significant fluctuations in tail latency, which have a severe negative impact to the end user experience. Therefore, some promising computing systems and effective scheduling solutions are proposed to guarantee better quality of service (QoS), e.g., La-beled von Neumann Architecture (LvNA) based cloud prototype system. However, relatively few work focuses on the tail latency observation and evaluation of public CCSs and newest prototype systems. Therefore, we introduce an evaluation framework named TailCmp which includes nine representative workloads ranging over different tail latency requirements and application domains. With the TailCmp, we can collect and analyze four evaluation metrics including tail latency entropy. In this paper, we conduct a large number of experiments with TailCmp on three CCSs (two public CCSs and one LvNA-based prototype system). The results show that the co-location on existing public CCSs can seriously affect the QoS of LC workloads, while the labeling mechanism in Lv Na - based prototype can improve the performance of LC workloads in co-location.

关键词： Cloud computing Limiting Processor scheduling Prototypes Tail Quality of service computer architecture

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