arXivDaily arXiv每日学术速递 周一至周五更新

科学与医疗

医学 AI

医学智能、临床 AI、医学影像、病理、诊断和医疗健康大模型。

2025-11-25 至 2025-11-25 共收录 71 信号源:cs.CV, cs.LG, q-bio, eess.IV, eess.SP

1. 医学影像 38 篇

2505.03114 2025-11-25 cs.CV 88%

Path and Bone-Contour Regularized Unpaired MRI-to-CT Translation

路径和骨轮廓正则化的无配对MRI到CT翻译

Teng Zhou, Jax Luo, Yuping Sun, Yiheng Tan, Shun Yao, Nazim Haouchine, Scott Raymond

机构 * School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China(计算机科学与技术学院,广东技术大学,广州,中国) Neurological Institute, Cleveland Clinic, OH, USA(神经医学研究院,克利夫兰医学中心,俄亥俄州,美国) Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China(神经外科,中山大学附属第一医院,广州,中国) Brigham and Women’s Hospital, Harvard Medical School, MA, USA(布里奇沃特医院,哈佛医学院,马萨诸塞州,美国) Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands(放射科,医学影像中心,格罗宁根大学医学中心,格罗宁根大学,荷兰)

专题命中 医学影像 :MRI(title,abstract);CT(title,abstract);分类 cs.CV

AI总结 本文提出了一种路径和骨轮廓正则化的无配对MRI到CT翻译方法,通过神经普通微分方程建模连续流,并利用骨轮廓生成网络提升骨结构翻译精度。

Journal ref Comput. Med. Imag. Graph. (2025) 102656

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2503.05060 2025-11-25 cs.CL 88%

ModernBERT is More Efficient than Conventional BERT for Chest CT Findings Classification in Japanese Radiology Reports

ModernBERT在日语放射学报告中的胸部CT发现分类中比传统BERT更高效

Yosuke Yamagishi, Tomohiro Kikuchi, Shouhei Hanaoka, Takeharu Yoshikawa, Osamu Abe

专题命中 医学影像 :CT(title,abstract);radiology(title,abstract)

AI总结 ModernBERT在日语放射学报告中的胸部CT发现分类中表现出更高的效率和性能,但在领域转移情况下表现不如传统BERT。

Comments 31 pages

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2511.18654 2025-11-25 cs.CV 85%

From Healthy Scans to Annotated Tumors: A Tumor Fabrication Framework for 3D Brain MRI Synthesis

从健康扫描到标注肿瘤:一种用于3D脑部MRI合成的肿瘤生成框架

Nayu Dong, Townim Chowdhury, Hieu Phan, Mark Jenkinson, Johan Verjans, Zhibin Liao

机构 * Australian Institute for Machine Learning(澳大利亚机器学习研究所) The University of Adelaide(阿德莱德大学) School of Computer and Mathematical Sciences(计算机与数学科学学院)

专题命中 医学影像 :MRI(title,abstract);clinical AI(abstract);medical image(abstract);分类 cs.CV

AI总结 本文提出了一种名为Tumor Fabrication的新型框架,通过无配对的3D脑肿瘤合成方法,利用健康图像和少量真实标注数据生成大量合成数据,以提升低数据环境下的肿瘤分割性能。

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2511.17803 2025-11-25 cs.CV cs.AI 85%

Pillar-0: A New Frontier for Radiology Foundation Models

Pillar-0:放射学基础模型的新前沿

Kumar Krishna Agrawal, Longchao Liu, Long Lian, Michael Nercessian, Natalia Harguindeguy, Yufu Wu, Peter Mikhael, Gigin Lin, Lecia V. Sequist, Florian Fintelmann, Trevor Darrell, Yutong Bai, Maggie Chung, Adam Yala

专题命中 医学影像 :radiology(title,abstract);MRI(abstract);CT(abstract);分类 cs.CV

AI总结 Pillar-0通过预训练大量医学影像数据和RATE框架,在放射学任务中实现高性能表现,超越现有模型并扩展至新任务。

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2511.19183 2025-11-25 cs.CV 83%

nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation

nnActive: 一种用于评估3D生物医学分割中主动学习的框架

Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl, Till J. Bungert, Lukas Klein, Lars Krämer, Paul F. Jaeger, Fabian Isensee, Klaus Maier-Hein

机构 * German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing(德国癌症研究中心(DKFZ)海德堡,医学影像计算部门) Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany(海德堡影像学,德国癌症研究中心(DKFZ),海德堡,德国) Faculty of Mathematics and Computer Science, University of Heidelberg, Germany(海德堡大学数学与计算机科学学院,德国) German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems(德国癌症研究中心(DKFZ)海德堡,智能医学系统部门) Institute for Machine Learning, ETH Zürich, Switzerland(苏黎世联邦理工学院机器学习研究所,瑞士) German Cancer Research Center (DKFZ) Heidelberg, Interactive Machine Learning Group(德国癌症研究中心(DKFZ)海德堡,交互式机器学习小组) Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Germany(放射肿瘤学系模式分析与学习小组,海德堡大学医院,德国) National Center for Tumor Diseases (NCT) Heidelberg, Germany(海德堡肿瘤疾病国家中心(NCT),德国)

专题命中 医学影像 :biomedical(title,abstract);medical image(abstract);分类 cs.CV

AI总结 nnActive提出一种开源框架,通过大规模研究和改进的随机采样策略,评估3D生物医学分割中主动学习的性能与效率。

Comments Accepted at TMLR

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2511.19046 2025-11-25 cs.CV cs.AI 81%

MedSAM3: Delving into Segment Anything with Medical Concepts

MedSAM3:深入探索医学概念中的分割任务

Anglin Liu, Rundong Xue, Xu R. Cao, Yifan Shen, Yi Lu, Xiang Li, Qianqian Chen, Jintai Chen

专题命中 医学影像 :medical image(abstract);MRI(abstract);CT(abstract);biomedical(abstract)

AI总结 MedSAM3通过引入可文本提示的医学分割模型,实现了基于开放词汇文本描述的精准解剖结构分割,显著优于现有方法。

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2511.17421 2025-11-25 cs.CV cs.AI 81%

Preventing Shortcut Learning in Medical Image Analysis through Intermediate Layer Knowledge Distillation from Specialist Teachers

通过专家教师的中间层知识蒸馏防止医学图像分析中的捷径学习

Christopher Boland, Sotirios Tsaftaris, Sonia Dahdouh

机构 * Canon Medical Research Europe(康特拉医疗研究欧洲公司) School of Engineering, The University of Edinburgh(爱丁堡大学工程学院)

专题命中 医学影像 :medical image(title,abstract);biomedical(comments,journal_ref);分类 cs.CV

AI总结 通过专家教师的中间层知识蒸馏方法,有效缓解医学图像分析中的捷径学习问题,提升模型鲁棒性和临床实用性。

Comments Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2025:020

Journal ref Machine.Learning.for.Biomedical.Imaging. 3 (2025)

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2511.18422 2025-11-25 cs.CV cs.LG 81%

NeuroVascU-Net: A Unified Multi-Scale and Cross-Domain Adaptive Feature Fusion U-Net for Precise 3D Segmentation of Brain Vessels in Contrast-Enhanced T1 MRI

NeuroVascU-Net:一种统一的多尺度和跨域自适应特征融合U-Net,用于精确的对比增强T1 MRI脑血管三维分割

Mohammad Jafari Vayeghan, Niloufar Delfan, Mehdi Tale Masouleh, Mansour Parvaresh Rizi, Behzad Moshiri

机构 * School of Electrical and Computer Engineering, College of Engineering, University of Tehran(电信工程学院,工程学院,德黑兰大学) Department of EECS, Lassonde School of Engineering, York University(电子工程系,拉索nde工程学院,约克大学) Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences(神经外科系,医学院,伊朗医学科学大学) Department of Electrical and Computer Engineering, University of Waterloo(电子工程系,滑铁库大学)

专题命中 医学影像 :MRI(title,abstract);分类 cs.CV、cs.LG

AI总结 NeuroVascU-Net通过多尺度和跨域自适应特征融合模块,实现高精度脑血管分割,适用于临床标准T1CE MRI,提升神经外科手术规划的准确性与效率。

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2511.17614 2025-11-25 cs.CV cs.LG 81%

HSMix: Hard and Soft Mixing Data Augmentation for Medical Image Segmentation

HSMix:用于医学图像分割的硬软混合数据增强

Danyang Sun, Fadi Dornaika, Nagore Barrena

机构 * University of the Basque Country(巴斯克大学) IKERBASQUE(ikerbasque研究所)

专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、cs.LG

AI总结 HSMix通过硬软混合数据增强技术提升医学图像分割性能,结合同质区域混合与亮度调整,增强数据多样性并保留语义信息。

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2511.13628 2025-11-25 eess.IV eess.SP physics.med-ph 81%

Smooth Total variation Regularization for Interference Detection and Elimination (STRIDE) for MRI

用于干扰检测和消除的平滑总变分正则化(STRIDE)用于MRI

Alexander Mertens, Diego Martinez, Amgad Louka, Ying Yang, Chad Harris, Ian Connell

专题命中 医学影像 :MRI(title,abstract);分类 eess.IV、eess.SP

AI总结 STRIDE通过利用MRI图像的平滑性改进EMI去除,提供更优的去噪性能,尤其对时间变化噪声源效果显著。

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2511.19425 2025-11-25 cs.CV 79%

SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation

SAM3-Adapter: 高效适应Segment Anything 3用于伪装物分割、阴影检测和医学图像分割

Tianrun Chen, Runlong Cao, Xinda Yu, Lanyun Zhu, Chaotao Ding, Deyi Ji, Cheng Chen, Qi Zhu, Chunyan Xu, Papa Mao, Ying Zang

机构 * KOKONI, Moxin (Huzhou) Tech. Co., LTD(摩西(湖州)科技有限公司) College of Computer Science and Technology, Zhejiang University(浙江大学计算机科学与技术学院) School of Computer Science and Engineering, Nanjing University of Science and Technology(南京理工大学计算机科学与工程学院) School of Information Engineering, Huzhou University(湖州大学信息工程学院) School of Electrical and Electronic Engineering, Nanyang Technological University(新加坡南洋理工大学电子与电气工程学院) College of Computing and Data Science, Nanyang Technological University(新加坡南洋理工大学计算与数据科学学院) School of Information Science and Technology, University of Science and Technology of China(中国科学技术大学信息科学与技术学院)

专题命中 医学影像 :medical image(title,abstract);分类 cs.CV

AI总结 SAM3-Adapter通过高效适配框架提升SAM3在伪装物分割、阴影检测和医学影像分割等任务中的性能。

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2511.19071 2025-11-25 cs.CV 79%

DEAP-3DSAM: Decoder Enhanced and Auto Prompt SAM for 3D Medical Image Segmentation

DEAP-3DSAM:解码器增强和自动提示的SAM用于3D医学图像分割

Fangda Chen, Jintao Tang, Pancheng Wang, Ting Wang, Shasha Li, Ting Deng

机构 * College of Computer Science and Tech.(计算机科学与技术学院) National University of Defense Tech.(国防科技大学) Department of GI Medical Onco.(胃肠医学肿瘤科) TMUCIH, NCRCC(TMUCIH、NCRCC)

专题命中 医学影像 :medical image(title,abstract);分类 cs.CV

AI总结 DEAP-3DSAM通过特征增强解码器和双注意力提示器,实现了3D医学图像分割的自动提示和高效分割。

Comments Accepted by BIBM 2024

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2511.18534 2025-11-25 cs.CV 79%

HiFi-MambaV2: Hierarchical Shared-Routed MoE for High-Fidelity MRI Reconstruction

HiFi-MambaV2:分层共享路由MoE用于高保真MRI重建

Pengcheng Fang, Hongli Chen, Guangzhen Yao, Jian Shi, Fangfang Tang, Xiaohao Cai, Shanshan Shan, Feng Liu

专题命中 医学影像 :MRI(title,abstract);分类 cs.CV

AI总结 HiFi-MambaV2通过分层共享路由MoE和频率一致拉普拉斯金字塔,实现高保真MRI重建,优于多种基线模型。

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2506.13292 2025-11-25 cs.CV cs.AI 79%

Automatic Multi-View X-Ray/CT Registration Using Bone Substructure Contours

自动多视角X射线/CT配准使用骨亚结构轮廓

Roman Flepp, Leon Nissen, Bastian Sigrist, Arend Nieuwland, Nicola Cavalcanti, Philipp Fürnstahl, Thomas Dreher, Lilian Calvet

机构 * University Children’s Hospital Zürich(苏黎世儿童医院大学医院) University Hospital Balgrist, University of Zurich(苏黎世大学医院巴尔格里斯分院) Department of Orthopedic Surgery(骨科部门)

专题命中 医学影像 :CT(title,abstract);分类 cs.CV

AI总结 本研究提出了一种基于骨亚结构轮廓的自动多视角X射线/CT配准方法,实现了亚毫米精度并提高了实用性。

Comments This paper was accepted to IPCAI 2025. The Project Webpage is: https://rflepp.github.io/BoneSubstructureContours2D3DRegistration/

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2511.18358 2025-11-25 eess.SP 79%

CT-CFAR A Robust CFAR Detector Based on CLEAN and Truncated Statistics in Sidelobe-Contaminated Environments

基于CLEAN和截断统计的CT-CFAR稳健检测器:在旁瓣污染环境中的一种稳健CFAR检测算法

Jiachen Zhu, Fangjiong Chen, Jie Wu, Ming Xia

专题命中 医学影像 :CT(title,abstract);分类 eess.SP

AI总结 本文提出一种基于CLEAN和截断统计的CT-CFAR检测器,用于在旁瓣污染环境下实现高精度目标检测,提升鲁棒性和计算效率。

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2511.18208 2025-11-25 cs.CV 79%

Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRI

大规模预训练使多模态AI能够区分放射性坏死与脑转移瘤进展的常规MRI

Ahmed Gomaa, Annette Schwarz, Ludwig Singer, Arnd Dörfler, Matthias Stefan May, Pluvio Stephan, Ishita Sheth, Juliane Szkitsak, Katharina Breininger, Yixing Huang, Benjamin Frey, Oliver Schnell, Daniel Delev, Roland Coras, Daniel Höfler, Philipp Schubert, Jenny Stritzelberger, Sabine Semrau, Andreas Maier, Dieter H Heiland, Udo S. Gaipl, Andrea Wittig, Rainer Fietkau, Christoph Bert, Stefanie Corradini, Florian Putz

专题命中 医学影像 :MRI(title,abstract);分类 cs.CV

AI总结 本研究通过大规模预训练和多模态深度学习策略,利用常规MRI区分放射性坏死与肿瘤进展,实现高准确率的可解释AI解决方案。

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2511.17988 2025-11-25 cs.CV cs.IR 79%

HyM-UNet: Synergizing Local Texture and Global Context via Hybrid CNN-Mamba Architecture for Medical Image Segmentation

HyM-UNet: 通过混合CNN-Mamba架构协同局部纹理和全局上下文进行医学图像分割

Haodong Chen, Xianfei Han, Qwen

机构 * School of Information and Communication Engineering, Beijing University of Posts and Communications (BUPT)(信息与通信工程学院,北京邮电大学) Alibaba Cloud AI Labs(阿里云人工智能实验室)

专题命中 医学影像 :medical image(title);diagnosis(abstract);分类 cs.CV

AI总结 HyM-UNet通过混合CNN-Mamba架构,结合局部纹理和全局上下文,提升医学图像分割的精度与效率。

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2511.17873 2025-11-25 eess.IV 79%

TransLK-Net: Entangling Transformer and Large Kernel for Progressive and Collaborative Feature Encoding and Decoding in Medical Image Segmentation

TransLK-Net: 将变换器与大核结合以实现医学图像分割中的渐进性和协作性特征编码与解码

Jin Yang, Daniel S. Marcus, Aristeidis Sotiras

专题命中 医学影像 :medical image(title,abstract);分类 eess.IV

AI总结 TransLK-Net通过结合变换器与大核卷积,改进医学图像分割的特征编码与解码过程。

Comments 7 figures

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2511.19226 2025-11-25 physics.med-ph 78%

In-vivo imaging with a low-cost MRI scanner and cloud data processing in low-resource settings

在低资源环境中使用低成本MRI扫描仪和云数据处理进行体内成像

Teresa Guallart-Naval, Robert Asiimwe, Patricia Tusiime, Mary A. Nassejje, Leo Kinyera, Lemi Robin, Maureen Nayebare, Luiz G. C. Santos, Marina Fernández-García, Lucas Swistunow, José M. Algarín, John Stairs, Michael Hansen, Ronald Amodoi, Andrew Webb, Joshua Harper, Steven J. Schiff, Johnes Obungoloch, Joseba Alonso

专题命中 医学影像 :MRI(title,abstract)

AI总结 本研究展示了在低资源环境中通过改进硬件和软件实现低成本MRI系统获得临床相关图像质量的可行性。

Comments 10 pages, 10 figures, comments welcome

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2511.18197 2025-11-25 eess.IV cs.CV 76%

Linear Algebraic Approaches to Neuroimaging Data Compression: A Comparative Analysis of Matrix and Tensor Decomposition Methods for High-Dimensional Medical Images

神经影像数据压缩的线性代数方法:矩阵和张量分解方法在高维医学图像中的比较分析

Jaeho Kim, Daniel David, Ana Vizitiv

专题命中 医学影像 :medical image(title);分类 cs.CV、eess.IV

AI总结 本文比较了Tucker分解和SVD在神经影像数据压缩中的性能,发现Tucker分解在保持多维关系和重建保真度方面更优,而SVD在极端压缩中表现突出但保真度较低。

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2507.19165 2025-11-25 eess.IV cs.CV 76%

Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge

极端呼吸运动下的心脏MRI分析:CMRxMotion挑战结果

Kang Wang, Chen Qin, Zhang Shi, Haoran Wang, Xiwen Zhang, Chen Chen, Cheng Ouyang, Chengliang Dai, Yuanhan Mo, Chenchen Dai, Xutong Kuang, Ruizhe Li, Xin Chen, Xiuzheng Yue, Song Tian, Alejandro Mora-Rubio, Kumaradevan Punithakumar, Shizhan Gong, Qi Dou, Sina Amirrajab, Yasmina Al Khalil, Cian M. Scannell, Lexiaozi Fan, Huili Yang, Xiaowu Sun, Rob van der Geest, Tewodros Weldebirhan Arega, Fabrice Meriaudeau, Caner Özer, Amin Ranem, John Kalkhof, İlkay Öksüz, Anirban Mukhopadhyay, Abdul Qayyum, Moona Mazher, Steven A Niederer, Carles Garcia-Cabrera, Eric Arazo, Michal K. Grzeszczyk, Szymon Płotka, Wanqin Ma, Xiaomeng Li, Rongjun Ge, Yongqing Kou, Xinrong Chen, He Wang, Chengyan Wang, Wenjia Bai, Shuo Wang

机构 * Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, Shanghai 200032, China Shanghai Key Laboratory of MICCAI, Fudan University, Shanghai, Shanghai 200032, China Department of Electrical Electronic Engineering \& I-X, Imperial College London, London, London SW7 2AZ, United Kingdom Department of Radiology, Zhongshan Hospital Affiliated to Fudan University, Shanghai, Shanghai 200032, China Department of Computing, Imperial College London, London, London SW7 2AZ, United Kingdom School of Computer Science, University of Sheffield, Sheffield, S1 4DP, United Kingdom Department of Engineering Science, University of Oxford, Oxford, OX2 0ES, United Kingdom Shanghai Pudong Hospital Human Phenome Institute, Fudan University, Shanghai, 201203, China School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, United Kingdom Diagnostic Imaging, University of Alberta, Edmonton, AB T6G 1K4, Canada Department of Computer Science Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong 000000, China The D-Lab, Department of Precision Medicine, GROW - Research Institute for Oncology Reproduction, Maastricht University, 6220 MD Maastricht, The Netherlands Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven 5612 AZ, The Netherlands Department of Radiology, Northwestern University, 737 N. Michigan Ave, Suite 1600, Chicago 60611, United States United Imaging Research, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China Division of Image Processing, Department of Radiology, Leiden University Medical Center, PO Box 9600, Leiden 2300 RC, The Netherlands Université Bourgogne Europe, CNRS, ICMUB UMR 6302, 21000 Dijon, France Istanbul Technical University, Maslak, 34467, İstanbul, Türkiye Computer Science, Technical University of Darmstadt, Karolinenpl. 5, 64289 Darmstadt, Germany Lung Institute, Faculty of Medicine, Imperial College London, Guy Scadding Building, Cale Street, London, SW3 6LY,United Kingdom Hawkes Institute, Department of Computer Science, University College London, 66-72 Gower St, London, United Kingdom School of Medicine, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland CeADAR: Ireland's Centre for AI, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland Sano Centre for Computational Medicine, Czarnowiejska 36, 30-054, Krakow, Poland Faculty of Mathematics Computer Science, Jagiellonian University, S. Łojasiewicza 6, Krakow, Poland Department of Electronic Computer Engineering, The Hong Kong University of Science School of Instrument Science Engineering, Southeast University, Nanjing, Nanjing 210096, China College of Artificial Intelligence, Nanjing University of Aeronautics Academy for Engineering Technology, Fudan University, Shanghai, Shanghai 200433, China College of Biomedical Engineering, Fudan University, Shanghai, Shanghai 200433, China Institute of Science Technology for Brain-inspired Intelligence, Fudan University, Shanghai, Shanghai 200433, China Department of Brain Sciences, Imperial College London, London, London SW7 2AZ, United Kingdom Data Science Institute, Imperial College London, London, London SW7 2AZ, United Kingdom

专题命中 医学影像 :MRI(title);分类 cs.CV、eess.IV

AI总结 本文提出CMRxMotion挑战,通过公开数据集评估深度学习模型在呼吸运动干扰下的心脏MRI分析性能,并探讨运动伪影对临床生物标志物的影响。

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2409.13930 2025-11-25 eess.IV cs.CV 76%

RN-SDEs: Limited-Angle CT Reconstruction with Residual Null-Space Diffusion Stochastic Differential Equations

RN-SDEs: 有限角度CT重建与残差零空间扩散随机微分方程

Jiaqi Guo, Santiago Lopez-Tapia, Wing Shun Li, Yunan Wu, Marcelo Carignano, Martin Kröger, Vinayak P. Dravid, Igal Szleifer, Vadim Backman, Aggelos K. Katsaggelos

机构 * Department of Electrical and Computer Engineering, Northwestern University(电气与计算机工程系,西北大学) Department of Materials Science and Engineering, Northwestern University(材料科学与工程系,西北大学) Applied Physics Program, Northwestern University(应用物理项目,西北大学)

专题命中 医学影像 :CT(title);分类 cs.CV、eess.IV

AI总结 本文提出RN-SDEs方法,通过均值回复随机微分方程和范围-零空间分解技术,实现有限角度CT重建中的高质量图像恢复和优越性能。

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2503.19152 2025-11-25 eess.IV cs.AI cs.CV 73%

PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation

PSO-UNet: 基于粒子群优化的U-Net框架用于精确多模态脑肿瘤分割

Shoffan Saifullah, Rafał Dreżewski

机构 * Faculty of Computer Science, AGH University of Krakow(计算机科学系,克拉科夫AGH大学) Department of Informatics, Universitas Pembangunan Nasional Veteran Yogyakarta(信息系,全国 veterans 大学 Yogya 市)

专题命中 医学影像 :medical image(abstract);MRI(abstract);分类 cs.CV、eess.IV

AI总结 PSO-UNet通过粒子群优化与U-Net结合,实现多模态脑肿瘤分割的高精度和高效优化。

Comments 9 pages, 6 figures, 4 tables, Gecco 2025 Conference

Journal ref GECCO '25 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025

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2401.00728 2025-11-25 eess.IV cs.CV cs.LG 67%

MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification

MultiFusionNet:多层多模态融合的深度神经网络用于胸部X光图像分类

Saurabh Agarwal, K. V. Arya, Yogesh Kumar Meena

专题命中 医学影像 :medical image(abstract);分类 cs.CV、cs.LG、eess.IV

AI总结 MultiFusionNet通过多层多模态融合和FDSFM模块提升胸部X光图像分类的准确率,达到97.21%和99.60%

Comments 19 pages

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2505.06073 2025-11-25 eess.SP eess.IV 62%

Smooth optimization using global and local low-rank regularizers

利用全局和局部低秩正则化进行平滑优化

Rodrigo A. Lobos, Javier Salazar Cavazos, Raj Rao Nadakuditi, Jeffrey A. Fessler

专题命中 医学影像 :MRI(abstract);分类 eess.IV、eess.SP

AI总结 本文提出用Huber型函数替代核范数,通过理论框架建立平滑正则化器的性质,并利用闭合表达式和新型步长策略提升优化效率。

Comments 41 pages, 7 figures

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2310.06339 2025-11-25 eess.IV cs.LG 62%

Automatic nodule identification and differentiation in ultrasound videos to facilitate per-nodule examination

在超声视频中自动识别和区分病灶以促进单病灶检查

Siyuan Jiang, Yan Ding, Yuling Wang, Lei Xu, Wenli Dai, Wanru Chang, Jianfeng Zhang, Jie Yu, Jianqiao Zhou, Chunquan Zhang, Ping Liang, Dexing Kong

专题命中 医学影像 :diagnosis(abstract);分类 cs.LG、eess.IV

AI总结 本文提出了一种基于深度学习的超声视频病灶重识别系统,通过特征提取和实时聚类算法实现病灶的自动识别与区分,以提高单病灶检查的效率。

Comments The authors wish to withdraw this manuscript as it requires major revisions that substantially change the methodology and conclusions. A significantly updated version of this work may be submitted elsewhere at a later date. Thank you for your understanding

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2511.18847 2025-11-25 cs.CV cs.AI 57%

Personalized Federated Segmentation with Shared Feature Aggregation and Boundary-Focused Calibration

具有共享特征聚合和边界聚焦校准的个性化联邦分割

Ishmam Tashdeed, Md. Atiqur Rahman, Sabrina Islam, Md. Azam Hossain

机构 * Islamic University of Technology(伊斯兰科技大学)

专题命中 医学影像 :medical image(abstract);分类 cs.CV

AI总结 本文提出FedOAP,通过共享特征聚合和边界聚焦校准,提升多器官肿瘤分割的个性化联邦学习性能。

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2511.18781 2025-11-25 cs.CV cs.AI 57%

A Novel Dual-Stream Framework for dMRI Tractography Streamline Classification with Joint dMRI and fMRI Data

一种用于结合dMRI和fMRI数据的新型双流框架用于dMRI束追踪分类

Haotian Yan, Bocheng Guo, Jianzhong He, Nir A. Sochen, Ofer Pasternak, Lauren J O'Donnell, Fan Zhang

机构 * University of Electronic Science and Technology of China(电子科技大学) Zhejiang University of Technology(浙江工业大学) University of Tel Aviv(特拉维夫大学) Harvard Medical School(哈佛医学院)

专题命中 医学影像 :MRI(abstract);分类 cs.CV

AI总结 本文提出一种结合dMRI和fMRI的双流框架,用于提升白质束分区的功能一致性,通过预训练网络和辅助网络实现更精确的束分类。

Comments Submitted to ISBI 2026, 7 pages, 2 figures

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2511.13533 2025-11-25 cs.CV 57%

Minimax Multi-Target Conformal Prediction with Applications to Imaging Inverse Problems

最小最大多目标符合预测及其在成像反问题中的应用

Jeffrey Wen, Rizwan Ahmad, Philip Schniter

机构 * Department of Electrical and Computer Engineering(电气与计算机工程系) The Ohio State University(俄亥俄州立大学) Department of Biomedical Engineering(生物医学工程系)

专题命中 医学影像 :MRI(abstract);分类 cs.CV

AI总结 本文提出了一种渐近最小最大方法用于多目标符合预测,应用于多指标盲图像质量评估、多任务不确定性量化和多轮测量获取,通过合成和MRI数据验证了其有效性。

Journal ref Transactions on Machine Learning Research, 11/2025. https://openreview.net/forum?id=53FEYwDQK0

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2502.07331 2025-11-25 cs.CV 57%

ERANet: Edge Replacement Augmentation for Semi-Supervised Meniscus Segmentation with Prototype Consistency Alignment and Conditional Self-Training

ERANet:边缘替换增强用于半监督半月板分割的原型一致性对齐与条件自训练

Siyue Li, Yongcheng Yao, Junru Zhong, Shutian Zhao, Fan Xiao, Tim-Yun Michael Ong, Ki-Wai Kevin Ho, James F. Griffith, Yudong Zhang, Shuihua Wang, Jin Hong, Weitian Chen

专题命中 医学影像 :MRI(abstract);分类 cs.CV

AI总结 ERANet通过边缘替换增强、原型一致性对齐和条件自训练策略,提升半监督半月板分割的性能和鲁棒性。

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