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。

2026-05-14 至 2026-05-14 共收录 18 信号源:cs.CV, cs.LG, q-bio, eess.IV, eess.SP

1. 医学影像 18 篇

2605.12560 2026-05-14 eess.IV cs.CV cs.LG 91%

Brain Tumor Classification in MRI Images: A Computationally Efficient Convolutional Neural Network

脑MRI图像中的肿瘤分类:一种计算高效的卷积神经网络

Md Fahimul Kabir Chowdhury, Jannatul Ferdous

机构 * Department of Computer Science and Engineering, University of North Texas, USA(北卡罗来纳州立大学计算机科学与工程系) Department of Electrical and Electronic Engineering, International Islamic University Chittagong, Bangladesh(伊斯兰国际大学查塔格昂分校电子与电气工程系)

专题命中 医学影像 :MRI(title,title_cn);diagnosis(abstract);分类 cs.CV、cs.LG、eess.IV;biomedical(journal_ref)

AI总结 本文提出一种轻量高效的卷积神经网络,用于多类脑肿瘤分类,利用MRI图像实现胶质瘤、脑膜瘤、垂体瘤和健康样本的准确分类,取得高准确率和ROC分数,同时参数更少。

Journal ref 2025 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), pp. 633-638, 2025

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2407.06527 2026-05-14 physics.med-ph 90%

Low-dose, high-resolution CT of infant-sized lungs via propagation-based phase contrast

通过传播基相位对比技术实现婴儿肺部低剂量高分辨率CT

James A. Pollock, Kaye Morgan, Linda C. P. Croton, Emily J. Pryor, Kelly J. Crossley, Christopher J. Hall, Daniel Hausermann, Anton Maksimenko, Stuart B. Hooper, Marcus J. Kitchen

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

AI总结 利用传播基相位对比成像技术,优化了婴儿肺部CT成像的分辨率与辐射剂量,实现了比现有高分辨率CT更优的图像质量,符合澳大利亚婴儿胸部CT辐射标准。

Comments Updated to match published version

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2605.12562 2026-05-14 eess.IV cs.AI cs.CV 90%

Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation

通过跨窗口知识蒸馏揭示肺部CT中的潜在病理特征

Bo Peng, Wujian Xu, Kun Wang, Ximing Liao, Na Wang, Daqian Shi, Tian Li, Jing Gao, Johan Thygesen, Yingqun Ji, Honghan Wu

机构 * Institute of Health Informatics, University College London(伦敦大学学院健康信息学研究所) Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University(同济大学医学院 pulmonary and critical care medicine 部门,上海东方医院) Queen Mary University of London(伦敦女王玛丽大学) School of Health and Wellbeing, University of Glasgow(格拉斯哥大学健康与福祉学院)

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

AI总结 本文提出跨窗口知识蒸馏框架,通过教师模型学习最信息丰富的窗口,提升多窗口肺部CT分析的AUC,显著提高诊断性能。

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2605.13813 2026-05-14 cs.CV 90%

JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift

JANUS:基于解剖的门控用于在分布偏移下鲁棒的CT分诊

Lavsen Dahal, Yubraj Bhandari, Geoffrey Rubin, Joseph Y. Lo

机构 * Center for Virtual Imaging Trials, RAI Labs, Department of Radiology, Duke University, Durham NC 27708, USA Electrical Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708, USA Department of Mathematics, Trinity College of Arts \& Sciences, Duke University, Durham, NC 27708, USA Department of Radiology

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

AI总结 JANUS通过解剖引导门控将视觉嵌入与宏放射组学先验条件相结合,提升CT分诊在不同病理和机构偏移下的鲁棒性,实现更高的AUROC和AUPRC。

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2605.13560 2026-05-14 cs.LG 90%

Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks

基于贝叶斯物理信息神经网络的稀疏纵向CT数据肺癌肿瘤生长不确定性的预测

Lingfei Kong, Haoran Ma

机构 * Department of Mathematics, Vanderbilt University(范德比大学数学系) John A. Paulson School of Engineering and Applied Science, Harvard University(哈佛大学约翰·A·保罗森工程与应用科学学院)

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

AI总结 本文提出一种结合Gompertz生长动力学与低维贝叶斯推断的贝叶斯物理信息神经网络,用于从稀疏不规则纵向CT数据中预测肺癌肿瘤生长,通过两阶段推断策略估计后验预测分布和不确定性区间,实验显示模型能捕捉异质性肿瘤生长模式并保持合理预测精度。

Comments 8 pages, 15 figures

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2508.15034 2026-05-14 q-bio.QM 90%

An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis

人类胎儿大脑的MRI图谱:用于胎儿大脑MRI分析的参考和分割工具

Mahdi Bagheri, Clemente Velasco-Annis, Jian Wang, Razieh Faghihpirayesh, Shadab Khan, Camilo Calixto, Camilo Jaimes, Lana Vasung, Abdelhakim Ouaalam, Onur Afacan, Simon K. Warfield, Caitlin K. Rollins, Ali Gholipour

专题命中 医学影像 :MRI(title,title_cn);分类 q-bio

AI总结 本文介绍了CRL-2025胎儿大脑图谱,提供了更详细的解剖信息和分割工具,用于胎儿大脑MRI分析和神经发育研究。

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2605.13555 2026-05-14 physics.med-ph cs.AI 89%

Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report

生成放射治疗用合成计算机断层扫描:SynthRAD2025挑战报告

Viktor Rogowski, Maarten L. Terpstra, Niklas Wahl, Florian Kamp, Erik van der Bijl, Arthur Jr. Galapon, Christopher Kurz, Bowen Xin, Zhengxiang Sun, Hollie Min, Gregg Belous, Jason Dowling, Yan Xia, Siyuan Mei, Fuxin Fan, Arthur Longuefosse, Javier Sequeiro Gonzalez, Miguel Diaz Benito, Alvaro Garcia Martin, Fabien Baldacci, Valentin Boussot, Cédric Hémon, Jean-Claude Nunes, Jean-Louis Dillenseger, Zhiyuan Zhang, Jinghua Cai, Han Bing, Tan Zuopeng, Ricardo Brioso, Daniele Loiacono, Guillaume Landry, Adrian Thummerer, Matteo Maspero

机构 * Radiation Physics, Department of Hematology, Oncology Radiation Physics, Skåne University Hospital, Lund, Sweden Medical Radiation Physics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden Radiotherapy Department, University Medical Center Utrecht, Utrecht, The Netherlands Computational Imaging Group for MR Diagnostics \& Therapy, University Medical Center Utrecht, Utrecht, The Netherlands Division of Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany Heidelberg Institute for Radiation Oncology (HIRO) National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany Department of Radiation Oncology Cyberknife Center, University Hospital of Cologne, Cologne, Germany Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany Bavarian Cancer Research Center (BZKF), Munich, Germany Australian eHealth Research Center, CSIRO, Brisbane, Australia School of Computer Science, University of Sydney, Sydney, Australia Department of Orthodontics Pattern Recognition Lab, FAU Erlangen-Nuremberg, Germany RIKEN Center for Integrative Medical Sciences, Tokyo, Japan Erasmus Mundus Joint Master's Degree IPCVai, University of Bordeaux, France Computer Science, Huazhong University of Science Huazhong University of Science Canon Medical Systems (China) CO., LTD., Beijing, China Department of Radiation Oncology, Inselspital, Bern University Hospital University of Bern, Bern, Switzerland

专题命中 医学影像 :MRI(summary_cn,abstract);CT(summary_cn,abstract);medical image(comments)

AI总结 SynthRAD2025挑战赛评估了合成CT生成方法在头颈、胸腔和腹部2362名患者中的性能,通过图像相似性、分割和剂量学指标验证了深度学习在生成CT中的有效性,揭示了MRI到CT转换的持续挑战。

Comments 59 pages total: 26 pages main article + supplementary material; 8 figures in the main manuscript and 3 supplementary figures. Currently under review at the journal Medical Image Analysis (MIA)

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2605.12753 2026-05-14 eess.IV cs.CV cs.LG 89%

Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data

稀疏2D到密集3D弱监督学习的优化:应用于大体积体外MRI数据的多标签分割

Paul Hoareau, Kuan Yi Wang, Brandon Bujak, Roy Sun, Govind Nair, Irene Cortese, Charidimos Tsagkas, Daniel Reich, Julien Cohen-Adad

机构 * NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal(神经多极实验室,生物医学工程学院,蒙特利尔理工学院) École Centrale de Lyon(里昂中央理工学院) Mila - Quebec AI Institute(魁北克人工智能研究所) Functional Neuroimaging Unit, CRIUGM, University of Montreal(功能神经影像单元,CRIUGM,蒙特利尔大学) Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health(转化神经放射学部门,国家神经疾病与中风研究所,国家卫生研究院) Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel(神经学转化成像(ThINk)巴塞尔,生物医学工程系,医学院,巴塞尔大学医院和巴塞尔大学) Neurologic Clinic and Policlinic, Departments of Medicine, University Hospital Basel, Switzerland(神经科诊所和多科诊所,医学院,巴塞尔大学医院,瑞士) Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Switzerland(临床神经免疫学和神经科学巴塞尔研究中心(RC2NB),巴塞尔大学医院和巴塞尔大学,瑞士) National Institute of Neurological Disorders and Stroke, National Institutes of Health(国家神经疾病与中风研究所,国家卫生研究院) Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada(圣朱斯特医院研究中心,蒙特利尔大学,蒙特利尔,魁北克,加拿大) Quantitative MRI core facility, NINDS, NIH(定量MRI核心设施,NINDS,NIH) Experimental Immunotherapeutics Unit, Division of Neuroimmunology and Neurovirology, NINDS, NIH(实验免疫治疗单元,神经免疫学和神经病毒学部门,NINDS,NIH)

专题命中 医学影像 :MRI(title,title_cn);分类 cs.CV、cs.LG、eess.IV;biomedical(comments)

AI总结 本文研究了稀疏2D到密集3D弱监督学习中多类分割的正则化需求差异,通过2D教师网络生成伪标签训练3D学生网络,发现2D教师需强空间增强和软标签正则化以克服数据稀缺,但传播至3D学生网络会降低性能。

Comments 19 pages. Submitted to Machine Learning for Biomedical Imaging (MELBA). Code and models: https://github.com/ivadomed/model_seg_sc-gm-lesion_human_ms_exvivo_t2star

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2605.12917 2026-05-14 cs.CV cs.LG 88%

Adaptive Conformal Prediction for Reliable and Explainable Medical Image Classification

自适应置信区间用于可靠且可解释的医学图像分类

One Octadion, Novanto Yudistira, Lailil Muflikhah

机构 * Faculty of Computer Science, Universitas Brawijaya(博雅大学计算机科学学院)

专题命中 医学影像 :medical image(title);CT(abstract,abstract_cn);medical AI(abstract);pathology(abstract)

AI总结 本文提出自适应Lambda准则改进RAPS,提升医学图像分类的可靠性与可解释性,实验表明其在跨域验证中表现优异。

Comments To appear in IEA/AIE 2026 (Springer LNAI)

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2605.13686 2026-05-14 cs.CV cs.AI 84%

Cross Modality Image Translation In Medical Imaging Using Generative Frameworks

利用生成框架在医学影像中实现跨模态图像翻译

Giulia Romoli, Alessia Capoccia, Filippo Ruffini, Francesco Di Feola, Luca Boldrini, Arturo Chiti, Renato Cuocolo, Tugba Akinci D'Antonoli, Fatemeh Darvizeh, Marcello Di Pumpo, Bradley J. Erickson, Liu Fang, Deborah Fazzini, Paola Feraco, Fabrizia Gelardi, Francesco Gossetti, Ana Isabel Hernáiz Ferrer, Michail E. Klontzas, Seyedmehdi Payabvash, Katrine Riklund, Sara N. Strandberg, Valerio Guarrasi, Paolo Soda

机构 * Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University(诊断与介入部门、放射物理、生物医学工程,乌梅大学) Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma(人工智能与计算机系统单位,工程部门,罗马生物医学学院) Vita-Salute San Raffaele University(维塔-萨拉特·桑拉法埃莱大学) Department of Medicine, Surgery and Dentistry, University of Salerno(医学、外科和牙科部门,萨勒诺大学) Division of Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Basel(诊断和介入神经放射学部门,放射学部门,巴塞尔大学医院) Department of Pediatric Radiology, University Children’s Hospital Basel(儿科放射学部门,巴塞尔儿童医院) Department of Life Science and Public Health, Università Cattolica del Sacro Cuore(生命科学与公共健康部门,圣心大学) Athinoula A. Martinos Center for Biomedical Imaging(阿提诺拉A·马里诺斯生物医学成像中心) Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete(人工智能与转化成像(ATI)实验室,放射学部门,医学院,克里特大学) Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute(放射学部门,临床科学、介入和科技(CLINTEC)部门,卡罗林斯卡研究所) Columbia University Medical Center(哥伦比亚大学医学中心) Department of Diagnostics and intervention, Diagnostic radiology, Umeå University(诊断与介入部门,诊断放射学,乌梅大学)

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

AI总结 本文提出了一种标准化的3D医学图像跨模态翻译评估框架,比较了七种生成模型在不同数据集上的性能,揭示了GANs在不同任务中优于潜在生成模型,并发现合成影像在临床应用中难以区分。

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2301.12647 2026-05-14 physics.med-ph 82%

3DMPR -- A robust morphological approach for applying phase retrieval in proximity to highly-attenuating objects in CT

3DMPR -- 一种稳健的形态学方法,用于在接近高衰减物体时应用相位检索

J. A. Pollock, L. C. P. Croton, K. S. Morgan, K. J. Crossley, M. J. Wallace, G. A. Buckley, S. B. Hooper, M. J. Kitchen

专题命中 医学影像 :CT(title,summary_cn)

AI总结 本文提出3DMPR方法,结合形态学操作和相位检索,提升CT图像质量,减少辐射暴露,实现高精度3D成像。

Comments Updated to match published version

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2605.13798 2026-05-14 cs.CV 81%

VoxCor: Training-Free Volumetric Features for Multimodal Voxel Correspondence

VoxCor:无需训练的体积分量用于多模态体素对应

Guney Tombak, Ertunc Erdil, Ender Konukoglu

机构 * Biomedical Image Computing Group, ETH Zurich(生物医学图像计算组,苏黎世联邦理工学院) The LOOP Zurich – Medical Research Center(苏黎世医疗研究中心)

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

AI总结 VoxCor通过冻结的2D Vision Transformer基础模型生成可重用的体积分量表示,无需训练即可实现跨模态和跨主体的体素对应,提升跨模态和跨主体的转换性能。

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2605.13059 2026-05-14 cs.CV 81%

BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability

BrainAnytime: 基于解剖结构的跨模态预训练用于脑图像分析,支持任意模态可用性

Guangqian Yang, Tong Ding, Wenlong Hou, Yue Xun, Ye Du, Qian Niu, Shujun Wang

机构 * Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China(生物医学工程系,香港理工大学,香港特别行政区,中国) Department of Technology Management for Innovation, The University of Tokyo, Japan(创新技术管理系,东京大学,日本) Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China(数据科学与人工智能系,香港理工大学,香港特别行政区,中国)

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

AI总结 BrainAnytime通过跨模态蒸馏和解剖引导课程掩码,在共享的3D掩码自动编码器中学习MRI与PET的结构-分子对应关系,实现对任意模态可用性的统一预训练,提升多任务性能。

Comments Early accepted by MICCAI 2026

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2605.12650 2026-05-14 cs.CV 79%

CRAFT: Clinical Reward-Aligned Finetuning for Medical Image Synthesis

CRAFT:面向医学图像合成的临床对齐微调

Yunsung Chung, Alex El Darzi, Carlo El Khoury, Han Feng, Nassir Marrouche, Jihun Hamm

机构 * Department of Computer Science, Tulane University(路易斯安那大学计算机科学系) School of Medicine, Tulane University(路易斯安那大学医学院)

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

AI总结 本文提出CRAFT框架,通过临床对齐评分和奖励优化提升医学图像生成质量,减少幻觉生成,提升分类性能。

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2605.09020 2026-05-14 cs.CV 77%

The Direct Integration Theorem: A Rigorous Framework for Consistent Discrete Solutions of the Inverse Radon Problem

直接积分定理:逆Radon问题一致离散解的严格框架

Mikhail G. Mozerov

机构 * Institute for Information Transmission Problems, Russian Academy of Sciences(信息传输问题研究所,俄罗斯科学院)

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

AI总结 本文提出直接积分定理,通过经典中心切片定理推导,为CT中连续到离散转换提供数学一致方法,避免频域插值和常规梯度滤波,解决零频奇点和频域插值误差问题,实现高精度图像重建。

Comments Submitted to IEEE TPAMI. Code and data available at https://github.com/Mozerov-iitp/radon-dit/

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2605.12544 2026-05-14 physics.med-ph 75%

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data

双校正物理信息神经网络用于从稀疏数据重建脑血管血流

Jingtai Song, Qinsheng Zhu, Xiaodong Xing, Yufeng Tang, Zhiyun Zhang, Xianwen Zhang, Hao Wu

专题命中 医学影像 :MRI(abstract,abstract_cn);CT(abstract_cn)

AI总结 本文提出双校正物理信息神经网络,用于解决曲颈内动脉弯曲段血流重建难题,通过因果解耦策略和高阶物理损失函数提升稀疏数据下的重建精度与鲁棒性。

Comments 10 pages, 5 figures

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2603.24649 2026-05-14 cs.CV 70%

MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows

MedOpenClaw 和 MedFlowBench:在完整研究流程中审计医疗代理

Weixiang Shen, Chengzhi Shen, Yanzhu Hu, Che Liu, Junde Wu, Jiayuan Zhu, Xiao Han, Zongyue Li, Jingpei Wu, Min Xu, Daguang Xu, Yueming Jin, Benedikt Wiestler, Daniel Rueckert, Jiazhen Pan

机构 * Technical University of Munich(慕尼黑技术大学) TUM University Hospital(TUM大学医院) LMU Munich(慕尼黑大学) Imperial College London(伦敦帝国理工学院) University of Oxford(牛津大学) Carnegie Mellon University(卡内基梅隆大学) NVIDIA(NVIDIA公司) National University of Singapore(新加坡国立大学) Munich Center for Machine Learning(慕尼黑机器学习中心)

专题命中 医学影像 :pathology(abstract);radiology(abstract);分类 cs.CV

AI总结 本文提出 MedFlowBench 和 MedOpenClaw,用于评估医疗影像代理在完整研究流程中生成可审计证据的能力,发现仅依赖答案评分不够,需结合正确证据验证。

Comments 33 pages

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2510.14244 2026-05-14 eess.IV cs.AI cs.CV 62%

Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation

强化学习用于空间时间超声分割的无监督领域适应

Arnaud Judge, Nicolas Duchateau, Thierry Judge, Roman A. Sandler, Joseph Z. Sokol, Christian Desrosiers, Olivier Bernard, Pierre-Marc Jodoin

机构 * Department of Computer Science, University of Sherbrooke(谢布鲁克大学计算机科学系) INSA, Universite Claude Bernard Lyon 1, CNRS UMR 5220, Inserm U1206, CREATIS(里昂1大学INSA、CNRS UMR 5220、Inserm U1206、CREATIS) Dep. of Software and Information Technology Engineering, École de technologie supérieure(蒙特利尔工程学院软件与信息技术工程系) Institut Universitaire de France (IUF)(法国国家科学院(IUF))

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

AI总结 本文提出RL4Seg3D框架,通过强化学习提升空间时间超声分割的准确性、解剖有效性与时间一致性,并提供鲁棒的不确定性估计器。

Comments 13 pages, accepted for publication in IEEE TMI

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