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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

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Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

arXiv

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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An infinite class of Neumaier graphs and non-existence results

arXiv

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arXiv 2021年

作者： Abiad, Aida Castryck, Wouter de Boeck, Maarten Koolen, Jack H. Zeijlemaker, Sjanne Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Mathematics: Analysis Logic and Discrete Mathematics Ghent University Flanders Belgium Department of Mathematics and Data Science Vrije Universiteit Brussel Belgium Imec-COSIC Department of Electrical Engineering KU Leuven Belgium Department of Mathematics: Algebra and Geometry Ghent University Flanders Belgium School of Mathematical Sciences University of Science and Technology of China Hefei China CAS Wu Wen-Tsun Key Laboratory of Mathematics University of Science and Technology of China Hefei China

A Neumaier graph is a non-complete edge-regular graph containing a regular clique. A Neumaier graph that is not strongly regular is called a strictly Neumaier graph. In this work we present a new construction of strictly Neumaier graphs, and using Jacobi sums, we show that our construction produces infinitely many instances. Moreover, we prove some necessary conditions for the existence of (strictly) Neumaier graphs that allow us to show that several parameter sets are not admissible. Copyright © 2021, The Authors. All rights reserved.

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Non-invasive Cardiac Output Monitoring in Congenital Heart Disease

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Current Treatment Options in Pediatrics 2023年第4期9卷 247-259页

作者： Tandon, Animesh Bhattacharya, Sarnab Morca, Ayse Inan, Omer T Munther, Daniel S Ryan, Shawn D Latifi, Samir Q Lu, Nanshu Lasa, Javier J Marino, Bradley S Baloglu, Orkun Department of Heart Vascular and Thoracic Division of Cardiology and Cardiovascular Medicine Children’s Institute Cleveland Clinic Children’s Cleveland OH United States Cleveland Clinic Children’s Center for Artificial Intelligence (C4AI) Department of Heart Vascular and Thoracic Children’s Institute Cleveland Clinic Children’s Cleveland OH United States Department of Pediatrics Cleveland Clinic Lerner College of Medicine of Case Western Reserve University Cleveland OH United States Department of Biomedical Engineering Cleveland Clinic Lerner Research Institute and Case School of Engineering at Case Western Reserve University Cleveland OH United States Department of Electrical and Computer Engineering The University of Texas at Austin Austin 78712 TX United States Department of Integrated Hospital Care Division of Critical Care Children’s Institute Cleveland Clinic Children’s Cleveland OH United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States Department of Mathematics and Statistics Cleveland State University Cleveland 44115 OH United States Center for Applied Data Analysis and Modeling Cleveland State University Cleveland 44115 OH United States Department of Aerospace Engineering and Engineering Mechanics Texas Materials Institute The University of Texas at Austin Austin 78712 TX United States Department of Biomedical Engineering The University of Texas at Austin Austin 78712 TX United States Department of Mechanical Engineering Texas Materials Institute The University of Texas at Austin Austin 78712 TX United States Divisions of Cardiology and Critical Care Department of Pediatrics Children’s Medical Center UT Southwestern Medical Center Dallas TX United States

Purpose of review: Cardiac output (CO) is a fundamental physiological parameter that measures the volume of blood that is pumped by the heart per unit of time, and helps define how oxygen is delivered to the tissues of the human body. In this paper, we discuss current methods of continuous CO monitoring while defining low CO syndrome (LCOS) and how analytical tools may help improve CO management in the subpopulation of patients with congenital heart disease (CHD). Recent findings: Non-invasive methods of measuring CO have become increasingly available in recent years. Advantages of non-invasive over invasive techniques include decreased risk of procedural complications, decreased exposure to sedative and/or anesthetic agents, and increased patient comfort. Pediatric patient populations are particularly sensitive to the risks and complications of invasive techniques given the relative size of current technologies to pediatric vascular and cardiac dimensions. Summary: Novel device technologies, combined with emerging analytical techniques, may help improve measurement of CO in children and those with CHD, and allow earlier detection of LCOS. © 2023, The Author(s).

关键词： Cardiac index Cardiac output Congenital heart disease Non-invasive continuous cardiac output monitoring

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Addressing survey fatigue bias in longitudinal social contact studies to improve pandemic preparedness

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Scientific Reports 2025年第1期15卷 1-17页

作者： Dan, Shozen Ling, Zhi Chen, Yu Tegegne, Joshua Jaeger, Veronika K. Karch, André Mishra, Swapnil Ratmann, Oliver Vollmer, Sebastian Kraemer, Moritz Duchene, David A. Faria, Nuno Sejdinovic, Dino Unwin, Juliette Miscouridou, Xenia Semenova, Elizaveta Flaxman, Seth Bhatt, Samir Department of Mathematics Imperial College London London United Kingdom National University of Singapore Saw Swee Hock School of Public Health Singapore Singapore University of Münster Institute of Epidemiology and Social Medicine Münster Germany National University of Singapore Institute of Data Science Singapore Singapore Department of Infectious Disease Epidemiology Imperial College London London United Kingdom School of Public Health University of Copenhagen Copenhagen Denmark Department of Computer Science University of Oxford Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Mathematics and Statistics University of Cyprus Nicosia Cyprus School of Mathematics University of Bristol Bristol United Kingdom School of Computer and Mathematical Sciences The University of Adelaide Adelaide Australia MRC Centre for Global Infectious Disease Analysis Imperial College London London United Kingdom Department of Public Health University of Copenhagen Copenhagen Denmark Department of Biology Univesity of Oxford Oxford United Kingdom Technical University of Kaiserslautern Kaiserslautern Germany German Research Center for Artificial Intelligence Kaiserslautern Germany Department of Statistics University of Warwick Coventry United Kingdom

Social contact surveys are an important tool to assess infection risks within populations, and the effect of non-pharmaceutical interventions on social behaviour during disease outbreaks, epidemics, and pandemics. Numerous longitudinal social contact surveys were conducted during the COVID-19 era, however data analysis is plagued by survey fatigue, a phenomenon whereby the average number of social contacts reported declines with the number of repeat participations and as participants’ engagement decreases over time. Using data from the German COVIMOD Study between April 2020 to December 2021, we demonstrate that survey fatigue varied considerably by sociodemographic factors and was consistently strongest among parents reporting children contacts (parental proxy reporting), students, middle-aged individuals, those in full-time employment and those self-employed. We find further that, when using data from first-time participants as gold standard, statistical models incorporating a simple logistic function to control for survey fatigue were associated with substantially improved estimation accuracy relative to models with no survey fatigue adjustments, and that no cap on the number of repeat participations was required. These results indicate that existing longitudinal contact survey data can be meaningfully interpreted under an easy-to-implement statistical approach addressing survey fatigue confounding, and that longitudinal designs including repeat participants are a viable option for future social contact survey designs. © The Author(s) 2025.

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CONSTRUCTIONS OF COSPECTRAL GRAPHS WITH DIFFERENT ZERO FORCING NUMBERS

arXiv

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arXiv 2021年

作者： Abiad, Aida Brimkov, Boris Breen, Jane Cameron, Thomas R. Gupta, Himanshu Villagrán, Ralihe R. Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Mathematics: Analysis Logic and Discrete Mathematics Ghent University Belgium Department of Mathematics and Data Science Vrije Universiteit Brussel Belgium Department of Mathematics and Statistics Slippery Rock University United States Faculty of Science Ontario Tech University Canada Department of Mathematics Penn State Behrend United States Department of Mathematical Sciences University of Delaware United States Departamento de Matemáticas Centro de Investigación y de Estudios Avanzados del IPN Mexico

Several researchers have recently explored various graph parameters that can or cannot be characterized by the spectrum of a matrix associated with a graph. In this paper we show that several NP-hard zero forcing numbers are not characterized by the spectra of several types of associated matrices with a graph. In particular, we consider standard zero forcing, positive semidefinite zero forcing, and skew zero forcing, and provide constructions of infinite families of pairs of cospectral graphs which have different values for these numbers. We explore several methods for obtaining these cospectral graphs including using graph products, graph joins, and graph switching. Among these, we provide a construction involving regular adjacency cospectral graphs;the regularity of this construction also implies cospectrality with respect to several other matrices including the Laplacian, signless Laplacian, and normalized Laplacian. We also provide a construction where pairs of cospectral graphs can have an arbitrarily large difference between their zero forcing numbers. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

arXiv

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arXiv 2024年

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Heidelberg Division of Medical Image Computing Germany Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany West German Cancer Center Essen University Hospital Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/ Longitudinal-Difference-Weighting. © 2024, CC BY.

关键词： Image segmentation

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Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks

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Chemosphere 2024年 352卷 141484页

作者： Monteiro, Thalyssa Oliveira Alves, Pedro Augusto Araújo da Silva de Almeida Nava Barradas Filho, Alex Oliveira Villa-Vélez, Harvey Alexander Cruz, Glauber Postgraduate Program in Mechanical Engineering (PPGMEC) Department of Mechanics and Materials Federal Institute of Education Science and Technology of Maranhão (IFMA) Maranhão São Luís Brazil Postgraduate Program in Computer Science and Computational Mathematics (PPG-CCMC) Department of Computer Science University of São Paulo (USP) São Carlos São Paulo Brazil Data Analysis and Artificial Intelligence Laboratory (DARTi) Department of Computational Engineering Federal University of Maranhão (UFMA) Maranhão São Luís Brazil Department of Chemical Engineering Federal University of Maranhão (UFMA) Maranhão São Luís Brazil Processes and Thermal Systems Laboratory (LPSisTer) Department of Mechanical Engineering Federal University of Maranhão (UFMA) Maranhão São Luís Brazil

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. H... 详细信息

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO₂, NO_x, SO_x, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO₂, NO, and SO₂) produced during the direct biomass combustion (N₂/O₂:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001–12.41% MAPE, 0.001–5.82 mg Nm⁻³ MAE, and 0.03–52.30 mg Nm⁻³ RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO₂, NO, and SO₂ emissions from NEB biomasses were approximately 0.01%, 10⁻⁶%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models. © 2024 Elsevier Ltd

关键词： Brazilian northeastern Environmental statistics Evaluation metrics Gaseous emissions Predictive models Thermoconversion

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Low Rank Reduced Biquaternion Tensor Ring Decomposition and Tensor Completion

SSRN

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SSRN 2024年

作者： Luo, Hui Liu, Xin Liu, Wei Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Avenida Wai Long TaiPa 999078 China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology Avenida Wai Long TaiPa 999078 China School of Artificial Intelligence Sun Yat-sen University The Guangdong Key Laboratory of Big Data Analysis and Processing No.135 West Xingang Road Guangdong Provience Guangzhou510275 China Department of Mathematics University of Manitoba No.135 West Xingang Road WinnipegMBR3T 2N2 Canada

Reduced biquaternions (RBs), as an extension of complex numbers, have demonstrated exceptional capabilities in digital signal and image processing. They not only excel particularly in encoding color information but also feature multiplicative commutativity, which simplify the theoretical analysis of related algorithms. Concurrently, tensor completion methods based on tensor ring decomposition in the real domain have been proven effective in exploiting low-rank structures. Building upon these insights, this paper first introduces the Reduced Biquaternion Tensor Ring (RBTR) decomposition, along with a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completion algorithm RBTR-TV integrating RBTR rank with Total Variation (TV) regularization to optimize the process. In color image completion tasks, our method exhibits remarkable stability and robustness, outperforming traditional methods, especially if there are a lot of missing data in the image. © 2024, The Authors. All rights reserved.

关键词： Tensors

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

arXiv

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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

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