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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

Ensemble Learning Based Sleeping Cell Detection in Cloud Radio Access Networks

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Ensemble Learning Based Sleeping Cell Detection in Cloud Rad...

IEEE Symposium on computers and Communications (ISCC)

作者： Zhao Ming Chuan Sun Xiuhua Li Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

Sleeping cell problem refers to the degradation or unavailability of network services without triggered alarm, which is one of the most critical issues in current mobile networks. This problem is generally not detectable by the operators but only revealed after users' complaints occur. Therefore, it leads to the degradations of network performance in the service provision in the long run. To address this problem, we introduce a cloud-based sleeping cell detection platform into radio access networks (RANs) to detect the sleeping cells and deal with them automatically. In the cloud RANs (C-RANs), we combine and improve different methods employed in the pioneering studies in this field, and creatively use labeled training data and ensemble learning method for improving the accuracy. Particularly, we utilize expert optimization experience for further improving the detection framework. To evaluate the proposed ensemble learning based sleeping cell detection framework, we use a time-series dataset of Key Performance Indicator (KPI) in a real-world network. Trace-driven evaluation results show that the proposed framework can achieve up to 14.38% and 20.50% improvements compared with two existing schemes, respectively.

关键词：

DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

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arXiv 2023年

作者： Qian, Bo Chen, Hao Wang, Xiangning Che, Haoxuan Kwon, Gitaek Kim, Jaeyoung Choi, Sungjin Shin, Seoyoung Krause, Felix Unterdechler, Markus Hou, Junlin Feng, Rui Li, Yihao Daho, Mostafa El Habib Wu, Qiang Zhang, Ping Yang, Xiaokang Cai, Yiyu Li, Huating Jia, Weiping Sheng, Bin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ophthalmology Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China VUNO Inc. Seoul Korea Republic of AI/DX Convergence Business Group KT Korea Republic of Johannes Kepler University Linz Linz Austria School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China LaTIM UMR 1101 Inserm Brest France University of Western Brittany Brest France Department of Computer Science and Engineering The Ohio State University Ohio United States Department of Biomedical Informatics The Ohio State University Ohio United States Translational Data Analytics Institute The Ohio State University Ohio United States MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Shanghai China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Endocrinology and Metabolism Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China

computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

ZeroED: Hybrid Zero-shot Error Detection through Large Language Model Reasoning

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arXiv 2025年

作者： Ni, Wei Zhang, Kaihang Miao, Xiaoye Zhao, Xiangyu Wu, Yangyang Wang, Yaoshu Yin, Jianwei Center for Data Science Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Department of Data Science City University of Hong Kong Hong Kong Software College Zhejiang University Ningbo China Shenzhen Institute of Computing Sciences Shenzhen China College of Computer Science Zhejiang University Hangzhou China

Error detection (ED) in tabular data is crucial yet challenging due to diverse error types and the need for contextual understanding. Traditional ED methods often rely heavily on manual criteria and labels, making them labor-intensive. Large language models (LLM) can minimize human effort but struggle with errors requiring a comprehensive understanding of data context. In this paper, we propose ZeroED, a novel hybrid error detection framework, which combines LLM reasoning ability with the machine learning pipeline via zero-shot prompting. ZeroED operates in four steps, i.e., feature representation, error labeling, training data construction, and detector training. Initially, to enhance error distinction, ZeroED generates rich data representations using LLM-driven error reason-aware binary features, pre-trained embeddings, and statistical features. Then, ZeroED employs LLM to holistically label errors through in-context learning, guided by a two-step LLM reasoning process for detailed ED guidelines. To reduce token costs, LLMs are applied only to representative data selected via clustering-based sampling. High-quality training data is constructed through in-cluster label propagation and LLM augmentation with verification. Finally, a classifier is trained to detect all errors. Extensive experiments on seven datasets demonstrate that, ZeroED outperforms state-of-the-art methods by a maximum 30% improvement in F1 score and up to 90% token cost reduction. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning

Machine Knowledge and Human Cognition

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big data Mining and Analytics 2020年第4期3卷 292-299页

作者： Fashen Li Lian Li Jianping Yin Liang Huang Qingguo Zhou Ning An Yong Zhang Li Liu Jialin Zhang Kun Kuang Lei Yang Zhixi Wu Lianchun Yu the School of Physical Science and Technology Lanzhou UniversityLanzhou 730000China the School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China the School of Cyber Science and Engineering Dongguan University of TechnologyDongguan 523808China the Institute of Computational Physics and Complex Systems Lanzhou UniversityLanzhou 730000China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China the Department of Physics Xiamen UniversityXiamen 361005China the School of Big Data&Software Engineering Chongqing UniversityChongqing 400044China the Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China the College of Computer Science and Technology Zhejiang UniversityHangzhou 310000China the Institute ofModern Physics Chinese Academy of SciencesLanzhou 730000China

Intelligent machines are knowledge systems with unique knowledge structure and *** this paper,we discuss issues including the characteristics and forms of machine knowledge,the relationship between knowledge and human cognition,and the approach to acquire machine *** issues are of great significance to the development of artificial intelligence.

关键词： intelligent machine machine knowledge human cognition knowledge interpretation principle of functional similarity Probable Approximative Correction(PAC)model

DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

Abnormality Detection in Chest X-Ray Images Using Uncertainty Prediction Autoencoders 23rd

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Abnormality Detection in Chest X-Ray Images Using Uncertaint...

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Mao, Yifan Xue, Fei-Fei Wang, Ruixuan Zhang, Jianguo Zheng, Wei-Shi Liu, Hongmei School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Pazhou Lab Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783030597245

Chest radiography is widely used in annual medical screening to check whether lungs are healthy or not. Therefore it would be desirable to develop an intelligent system to help clinicians automatically detect potential abnormalities in chest X-ray images. Here with only healthy X-ray images, we propose a new abnormality detection approach based on an autoencoder which outputs not only the reconstructed normal version of the input image but also a pixel-wise uncertainty prediction. Higher uncertainty often appears at normal region boundaries with relatively larger reconstruction errors, but not at potential abnormal regions in the lung area. Therefore the normalized reconstruction error by the uncertainty provides a natural measurement for abnormality detection in images. Experiments on two chest X-ray datasets show the state-of-the-art performance by the proposed approach. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

An Audio-Visual dataset and Deep Learning Frameworks for Crowded Scene Classification

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arXiv 2021年

作者： Pham, Lam Ngo, Dat Nguyen, Phu X. Hoang, Truong Schindler, Alexander Competence Unit Data Science & Artificial Intelligence Center for Digital Safety & Security Austria Institute of Technology Austria School of Computer Science and Electronic Engineering University of Essex United Kingdom Department of Computer Fundamentals FPT University Ho Chi Minh City700000 Viet Nam FPT Software Company Limited Viet Nam

This paper presents a task of audio-visual scene classification (SC) where input videos are classified into one of five real-life crowded scenes: 'Riot', 'Noise-Street', 'Firework-Event', 'Music-Event', and 'Sport-Atmosphere'. To this end, we firstly collect an audio-visual dataset (videos) of these five crowded contexts from Youtube (in-the-wild scenes). Then, a wide range of deep learning frameworks are proposed to deploy either audio or visual input data independently. Finally, results obtained from high-performed deep learning frameworks are fused to achieve the best accuracy score. Our experimental results indicate that audio and visual input factors independently contribute to the SC task's performance. Significantly, an ensemble of deep learning frameworks exploring either audio or visual input data can achieve the best accuracy of 95.7%. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results 17th

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MIPI 2022 Challenge on Under-Display Camera Image Restorati...

17th European Conference on computer Vision, ECCV 2022

作者： Feng, Ruicheng Li, Chongyi Zhou, Shangchen Sun, Wenxiu Zhu, Qingpeng Jiang, Jun Yang, Qingyu Loy, Chen Change Gu, Jinwei Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Jiang, Biao Ding, Yuhan Li, WanZhang Feng, Xiaoyue Chen, Sijing Zhong, Tianheng Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang Nanyang Technological University Singapore Singapore SenseBrain San Jose United States Shanghai AI Laboratory Shanghai China SenseTime Research and Tetras.AI Beijing China University of Science and Technology of China Hefei China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology Hefei China School of Artificial Intelligence Xidian University Xi’an China School of Remote Sensing and Information Engineering Wuhan University Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Harbin Institute of Technology Harbin China University of Macau China Changsha Research Institute of Mining and Metallurgy Changsha China Tianjin University Tianjin China Computer Vision Lab University of Wurzburg Würzburg Germany Couger Inc. Tokyo Japan School of Statistics and Data Science Nankai University Tianjin China Founding Minds Software Thiruvananthapuram India Department of Electronics and Communication SRM University AP Amaravati India Fudan University Shanghai China Shenyang Aerospace University Shenyang China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783031250712

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image reconstruction