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。

共收录 43217 信号源:cs.CV, cs.LG, q-bio, eess.IV, eess.SP

1. 医学影像 22563 篇

2606.00689 2026-06-02 cs.CV 90%

Wavelet-Fusion Diffusion Model for Multimodal Brain MRI Synthesis with Modality and Metadata Conditioning

小波融合扩散模型用于多模态脑MRI合成,具有模态和元数据条件

Muhammad Nabi Yasinzai, Remika Mito, Mangor Pedersen

机构 * Department of Psychology & Neuroscience, Auckland University of Technology(心理学与神经科学系,奥克兰技术大学) Department of Psychiatry, University of Melbourne(精神病学系,墨尔本大学)

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

AI总结 提出一种小波融合扩散模型(WFDM),结合小波融合变分自编码器(WF-VAE)和条件3D U-Net扩散模型,通过显式模态和元数据条件实现多模态脑MRI合成,解决了数据集模态覆盖不均和异质性问题,在分布对齐上优于现有方法。

Comments 51 pages, 7 figures, including supplementary material. Submitted to Imaging Neuroscience

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2606.00156 2026-06-02 eess.IV cs.AI 90%

A physics-informed foundation model for quantitative diffusion MRI

一种用于定量扩散MRI的物理信息基础模型

Zihan Li, Jialan Zheng, Ziyu Li, Xun Yuan, Kasidit Anmahapong, Ziang Wang, Mingxuan Liu, Hongjia Yang, Yifei Chen, Zhuhao Wang, Yuhang He, Fang Chen, Rui Li, Huaiqiang Sun, Yi Liao, Congyu Liao, Yang Yang, Haibo Qu, Xue Zhang, Hongen Liao, Qiyuan Tian

机构 * School of Biomedical Engineering, Tsinghua University(清华大学生物医学工程系) Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford(牛津大学整合神经影像中心、FMRIB、临床神经科学系) Department of Radiology, West China Second University Hospital, Sichuan University(四川大学华西第二医院放射科) School of Biomedical Engineering and the Institute of Medical Robotics, Shanghai Jiaotong University(上海交通大学生物医学工程学院和医学机器人研究院) Department of Radiology, Institution of Radiology and Medical Imaging, West China Hospital, Sichuan University(四川大学华西医院放射科、放射医学与影像研究所) Department of Radiology and Biomedical Imaging, University of California San Francisco(加州大学旧金山分校放射科和生物医学影像系) Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine(斯坦福大学医学院精神病学与行为科学系)

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

AI总结 提出物理信息生成微结构网络(PIGMENT),通过零样本适应实现从稀疏数据中恢复可靠的定量扩散MRI参数映射。

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2606.00100 2026-06-02 cs.CV cs.AI 90%

CoilDrop-MRI: Self-supervised physics-guided MRI reconstruction with coil dropout

CoilDrop-MRI:基于线圈丢弃的自监督物理引导MRI重建

Tongxi Song, Ziyu Li, Zihan Li, Wen Zhong, Congyu Liao, Yang Yang, Hua Guo, Wenchuan Wu, Qiyuan Tian

机构 * School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University(清华大学生物医学工程系) Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford(牛津大学整合神经影像中心) Department of Radiology & Biomedical Imaging, University of California San Francisco(加州大学旧金山分校放射科与生物医学成像系)

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

AI总结 提出CoilDrop-MRI方法,通过在线圈维度进行丢弃并作为自监督训练目标,结合图像域和k空间域展开架构,实现无需全采样数据的并行MRI重建,在多站点、多场强、多模态数据集上性能优于现有自监督方法。

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2601.00212 2026-06-02 cs.CV 90%

IntraStyler: Intra-Domain Style Synthesis for Cross-Modality MRI Domain Adaptation

IntraStyler: 跨模态MRI域适应的域内风格合成

Han Liu, Yubo Fan, Hao Li, Dewei Hu, Daniel Moyer, Zhoubing Xu, Benoit M. Dawant, Ipek Oguz

机构 * Siemens Healthineers(西门子医疗) Princeton, NJ, USA(新泽西州普林斯顿) Vanderbilt University(范德比尔特大学) Mayo Clinic(梅奥诊所) Johnson & Johnson Innovative Medicine(强生创新医学)

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

AI总结 针对T2 MRI中前庭神经鞘瘤和耳蜗分割的域适应问题,提出IntraStyler方法,通过对比学习提取与解剖解耦的风格嵌入,自动发现并合成目标域内多样化的风格图像,提升下游分割模型的泛化性。

Comments Extension of our 1st place solution for the CrossMoDA 2023 challenge

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2605.30893 2026-06-01 cs.CV 90%

Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation

用于3D CT重建、增强和生成的基础VAE

Qi Chen, Shuhan Ding, Yu Gu, Nan Liu, Jiang Bian, Alan Yuille, Zongwei Zhou, Jingjing Fu

机构 * Department of Computer Science, Johns Hopkins University(约翰霍普金斯大学计算机科学系) Duke-NUS Medical School(duke-nus 医学院) Microsoft Research(微软研究院)

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

AI总结 本文发现,在自然图像上预训练的基础VAE可直接用于CT重建、增强和生成,无需训练或微调,通过冻结编解码器实现解剖结构保留和噪声抑制,并在分割和生成任务上取得显著提升。

Comments ICML 2026 Accepted

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2605.29163 2026-05-29 eess.IV 90%

BCER Agent: Reliable Long-Horizon MRI Workflow Execution via Compilation, Artifact Binding, and Bounded Local Recovery

BCER Agent: 通过编译、工件绑定和有界局部恢复实现可靠的长期MRI工作流执行

Ziyang Long, Xinqi Li, Junzhou Chen, Yifan Gao, Debiao Li, Hsin-Jung Yang

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

AI总结 提出BCER控制器架构,通过解耦高层规划与执行、有界局部恢复机制,在长链MRI工作流上实现端到端执行一致改进,并保持输出与中间工件的可审计关联。

Comments Pre-review submitted version of a paper accepted to MICCAI 2026. The final authenticated version will be available on SpringerLink

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2605.28016 2026-05-28 cs.CV physics.med-ph 90%

Enhancing Ultra-low-field MRI with Segmentation-guided Adversarial Learning

利用分割引导的对抗学习增强超低场MRI

James Grover, Andrew Phair, Michael Ferraro, David E. J. Waddington

机构 * Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health(Image X研究院,悉尼健康科学学院,医学与健康学院)

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

AI总结 提出结合解剖条件分割先验和模型集成的方法,通过Swin UNETR生成组织分割先验,并利用CycleGAN和T-REX两个增强网络合成3T级MRI,有效提升64 mT超低场MRI的图像质量。

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2605.25767 2026-05-28 cs.CV 90%

SAFE-Diff: Scale-Aware Attention and Feature-Dispersive Diffusion with Uncertainty Estimation for Contrast-Enhanced Breast MRI Synthesis

SAFE-Diff: 用于对比增强乳腺MRI合成的尺度感知注意力与特征分散扩散及不确定性估计

Tianyu Zhang, Xinglong Liang, Jarek van Dijk, Luyi Han, Chunyao Lu, Antonio Portaluri, Xinghe Xie, Yaofei Duan, Nika Rasoolzadeh, Xin Wang, Yuan Gao, Muzhen He, Yue Sun, Jonas Teuwen, Tao Tan, Ritse Mann

机构 * Department of Medical Imaging, Radboud University Medical Center(鲁文大学医学中心医学影像部) Department of Radiology, Netherlands Cancer Institute(荷兰癌症研究所放射科) Maastro Clinic(马斯垂克诊所) Faculty of Applied Science, Macao Polytechnic University(澳门理工大学应用科学学院) Department of Radiation Oncology, Netherlands Cancer Institute(荷兰癌症研究所放射肿瘤科)

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

AI总结 提出SAFE-Diff模型,通过尺度感知注意力、特征分散扩散和不确定性估计,解决对比增强乳腺MRI合成中复杂病灶纹理和异质性增强模式的挑战。

Comments Early accepted by MICCAI 2026

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2605.10583 2026-05-28 cs.CV 90%

FrequencyCT: Frequency Domain Self-supervised Low-dose CT Denoising

FrequencyCT:频域自监督低剂量CT去噪

Guoquan Wei, Liu Shi, Chong Chen, Qiegen Liu

机构 * School of Information Engineering, Nanchang University(南昌大学信息工程学院) SKLMS, ICMSEC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences(中国科学院数学与系统科学研究院SKLMS、ICMSEC)

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

AI总结 提出FrequencyCT,一种在频域中利用噪声与真实信号分布差异生成伪样本的零样本自监督方法,用于低剂量CT去噪,并通过数据截断稳定优化,实验验证了其临床潜力。

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2605.25589 2026-05-26 cs.CV 90%

Artifact Correction for Echo-Planar Imaging at Low-Field and Ultra-Low-Field MRI

低场和超低场MRI中回波平面成像的伪影校正

Sisi Qiao, Yilin Yu, Tiecheng Lin, Yuhao Liu, Jiajia Sun, Xiaoling Li

机构 * School of Mechanical Engineering, Xi'an Jiaotong University(西安交通大学机械工程学院)

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

AI总结 针对低场和超低场MRI中回波平面成像的奈奎斯特鬼影问题,提出一种无需参考扫描的校正流程,结合峰值对齐与插值重采样方法,有效抑制鬼影并提升图像质量。

Comments 19 pages, 10 figures, 2 tables

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2605.24625 2026-05-26 cs.CV 90%

ULF-Synth: Physics-Guided Ultra-Low-Field MRI Enhancement for Pediatric Neuroimaging

ULF-Synth:用于儿科神经影像的物理引导超低场MRI增强

Toufiq Musah, Salvatore Calcagno, Federica Proietto Salanitri, Xiaomeng Li, Maruf Adewole, Marawan Elbatel

机构 * Kwame Nkrumah University of Science and Technology(科拉努姆大学科学与技术学院) University of Catania(卡塔尼亚大学) The Hong Kong University of Science and Technology(香港科学与技术大学) Medical Artificial Intelligence Lab(医学人工智能实验室)

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

AI总结 提出ULF-Synth框架,通过从高场MRI合成逼真的超低场图像并采用空间-频率域目标,实现无需真实配对数据的超低场MRI增强,提升结构相似性和诊断可接受性。

Comments 10 pages, 2 figures, 3 tables

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2605.24371 2026-05-26 cs.CV cs.CL 90%

SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation

SliceWorld: 一种用于CT报告生成的预测性和可控世界状态模型

Yuanhe Tian, Yan Song

机构 * Zhongguancun Academy(中关村学院) University of Science and Technology of China(中国科学技术大学)

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

AI总结 提出SliceWorld世界状态框架,通过编码CT切片序列为因子感知的潜在状态,实现未来切片预测、病变因子干预和LLM报告生成,在M3D-Cap和CT-RATE上提升NLG指标和临床评估。

Comments 18 pages, 5 figures

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2601.14180 2026-05-25 cs.CV 90%

Progressive $\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising

渐进式 $\mathcal{J}$-不变自监督学习用于低剂量CT去噪

Yichao Liu, Zongru Shao, Yueyang Teng, Junwen Guo

机构 * organization= IWR, Heidelberg University , city= Heidelberg , postcode= 69120 , state= Baden Württemberg , country= Germany organization= Silicon Austria Labs , city= Linz , postcode= 4040 , state= Upper Austria , country= Austria organization= Institute of Science Tokyo , addressline= , city= Tokyo , country= Japan organization= College of Medicine Biological Information Engineering, Northeastern University , city= Shenyang , postcode= 110169 , state= Liaoning , country= China organization= Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education , city= Shenyang , postcode= 110169 , state= Liaoning , country= China organization= Department of Epidemiology \& Global Health, Umeå University , addressline= , city= Umeå , postcode= 90187 , country= Sweden

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

AI总结 提出渐进式 $\mathcal{J}$-不变学习,通过逐步盲点去噪机制和噪声注入正则化,提升低剂量CT去噪性能,在Mayo数据集上优于现有自监督方法并接近监督方法。

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2605.21906 2026-05-25 cs.CV 90%

Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining

从解剖到疾病表型的通用CT表示:通过聚合预训练

Yuheng Li, Yuan Gao, Haoyu Dong, Yuxiang Lai, Shansong Wang, Mojtaba Safari, James E. Baciak, Xiaofeng Yang

机构 * Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University(沃森·H·库勒生物医学工程系,佐治亚理工学院和埃默里大学) Department of Radiation Oncology and Winship Cancer Institute, Emory University(放射肿瘤学系和Winship癌症研究所,埃默里大学) Department of Electrical and Computer Engineering, Duke University(电气与计算机工程系,杜克大学) Department of Computer Science and Informatics, Emory University(计算机科学与信息学系,埃默里大学) Department of Materials Science & Engineering, Nuclear Engineering Program, University of Florida(材料科学与工程系、核工程项目,佛罗里达大学)

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

AI总结 提出FlexiCT系列CT基础模型,通过三阶段聚合连续预训练(二维轴向、三维解剖、报告引导语义对齐)统一CT分析,在分割、分类、配准、视觉语言理解和临床检索等任务上达到或超越专用模型,并捕获与肿瘤分期相关的影像特征。

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2604.11679 2026-05-25 cs.CV 90%

Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge

面向临床的大脑MRI基础模型:来自FOMO25挑战赛的发现

Asbjørn Munk, Stefano Cerri, Vardan Nersesjan, Christian Hedeager Krag, Jakob Ambsdorf, Pablo Rocamora García, Julia Machnio, Peirong Liu, Suhyun Ahn, Nasrin Akbari, Yasmina Al Khalil, Kimberly Amador, Sina Amirrajab, Tal Arbel, Meritxell Bach Cuadra, Ujjwal Baid, Bhakti Baheti, Jaume Banus, Kamil Barbierik, Christoph Brune, Yansong Bu, Baptiste Callard, Yuhan Chen, Cornelius Crijnen, Corentin Dancette, Peter Drotar, Prasad Dutande, Nils D. Forkert, Saurabh Garg, Jakub Gazda, Matej Gazda, Benoît Gérin, Partha Ghosh, Weikang Gong, Pedro M. Gordaliza, Sam Hashemi, Tobias Heimann, Fucang Jia, Jiexin Jiang, Emily Kaczmarek, Chris Kang, Seung Kwan Kang, Mohammad Khazaei, Julien Khlaut, Petros Koutsouvelis, Jae Sung Lee, Yuchong Li, Mengye Lyu, Mingchen Ma, Anant Madabhushi, Klaus H. Maier-Hein, Pierre Manceron, Andrés Martínez Mora, Moona Mazher, Felix Meister, Nataliia Molchanova, Steven A. Niederer, Leonard Nürnberg, Jinah Park, Abdul Qayyum, Jonas Richiardi, Antoine Saporta, Branislav Setlak, Ning Shen, Justin Szeto, Constantin Ulrich, Puru Vaish, Vibujithan Vigneshwaran, Leroy Volmer, Zihao Wang, Siqi Wei, Anthony Winder, Jelmer M. Wolterink, Maxence Wynen, Chang Yang, Si Young Yie, Mostafa Mehdipour Ghazi, Akshay Pai, Espen Jimenez Solem, Sebastian Nørgaard Llambias, Mikael Boesen, Michael Eriksen Benros, Juan Eugenio Iglesias, Mads Nielsen

机构 * organization= Department of Computer Science, University of Copenhagen , city= Copenhagen , country= Denmark organization= Pioneer Centre for AI , city= Copenhagen , country= Denmark organization= Copenhagen Research Centre for Biological Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital , region= Capital Region of Denmark , city= Copenhagen , country= Denmark organization= Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital Harvard Medical School , city= Boston , state= Massachusetts , country= USA Artificial Intelligence Laboratory, Massachusetts Institute of Technology , city= Boston , state= Massachusetts , country= USA organization= Johns Hopkins University , city= Baltimore , state= Maryland , country= USA organization= Radiological AI Testcenter (RAIT) , region= Capital Region of Denmark , city= Copenhagen , country= Denmark organization= Copenhagen University Hospital, Rigshospitalet , region= Capital Region of Denmark , city= Copenhagen , country= Denmark organization= Copenhagen University Hospital, Bispebjerg \& Frederiksberg Hospital , region= Capital Region of Denmark , city= Copenhagen , country= Denmark organization= Department of Clinical Medicine, Faculty of Health Medical Sciences, University of Copenhagen , city= Copenhagen , country= Denmark organization= Division of Medical Image Computing, German Cancer Research Center (DKFZ) , city= Heidelberg , country= Germany organization= University of British Columbia , city= Vancouver , state= British Columbia , country= Canada organization= Hawkes Institute, Department of Computer Science, University College London , city= London , country= United Kingdom Lung Institute, Faculty of Medicine, Imperial College London , city= London , country= United Kingdom organization= Department of Applied Mathematics, Technical Medical Centre, University of Twente , city= Enschede , country= Netherlands organization= IISLAB, Technical University of Košice , city= Košice , country= Slovakia organization= 2nd Department of Internal Medicine, Pavol Jozef Safarik University L Pasteur University Hospital , city= Košice , country= Slovakia organization= Fudan University , city= Shanghai , country= China organization= Shenzhen Technology University , city= Shenzhen , country= China organization= Department of Radiology, Lausanne University Hospital University of Lausanne , city= Lausanne , country= Switzerland organization= Louvain Neuroinflammation Imaging Lab (NIL), Université Catholique de Louvain , city= Brussels , country= Belgium organization= University of Applied Sciences organization= CIBM Center for Biomedical Imaging , city= Lausanne , country= Switzerland organization= Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology Reproduction, Maastricht University Medical Centre+ , city= Maastricht , country= The Netherlands organization= Department of Biomedical Engineering, Medical Image Analysis, Eindhoven University of Technology , city= Eindhoven , country= The Netherlands organization= Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , city= Shenzhen , country= China organization= McGill University Mila - Quebec AI Institute , city= Montreal , country= Canada organization= Hotchkiss Brain Institute Department of Radiology, University of Calgary , city= Calgary , state= Alberta , country= Canada organization= Department of Radiology, University of Calgary , city= Calgary , state= Alberta , country= Canada organization= Alberta Children's Hospital Research Institute, Department of Clinical Neuroscience, University of Calgary , city= Calgary , state= Alberta , country= Canada organization= The Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech Emory University , city= Atlanta , state= Georgia , country= USA organization= SGGS College of Engineering organization= Seoul National University , city= Seoul , country= South Korea organization= The D-Lab, Department of Precision Medicine, GROW Research Institute for Oncology Reproduction, Maastricht University , city= Maastricht , country= The Netherlands organization= Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School , city= Boston , state= Massachusetts , country= USA Nuclear Medicine, CARIM \& GROW, Maastricht University , city= Maastricht , country= The Netherlands organization= Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham Women’s Hospital, Harvard Medical School , city= Boston , state= Massachusetts , country= USA Learning Group, Heidelberg University Hospital , city= Heidelberg , country= Germany

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

AI总结 针对临床脑MRI数据异质且标注成本高的问题,FOMO25挑战赛通过自监督预训练(FOMO60K数据集)评估了16个团队的基础模型,发现自监督预训练能提升域迁移泛化性,但不同任务需不同预训练目标,且模型规模扩展收益有限。

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2603.24985 2026-05-25 cs.CV 90%

Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning

基于元学习的3D LGE MRI左心房壁少样本分割

Yusri Al-Sanaani, Rebecca Thornhill, Pablo Nery, Elena Pena, Robert deKemp, Calum Redpath, David Birnie, Sreeraman Rajan

机构 * Department of Systems and Computer Engineering, Carleton University(系统与计算机工程系,卡尔顿大学) Department of Radiology, Radiation Oncology, and Medical Physics, University of Ottawa(放射科、放射肿瘤学与医学物理系,渥太华大学) Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute(心内科,医学系,渥太华心脏研究所)

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

AI总结 提出基于模型无关元学习(MAML)和3D残差U-Net的框架,通过边界感知复合损失和辅助任务实现左心房壁的少样本分割,在5-shot下Dice达0.54,接近全监督性能。

Comments Accepted to IEEE EMBC 2026

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2605.22327 2026-05-22 cs.CV physics.med-ph 90%

Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learning

在MRI欠采样下,基于k空间的深度学习改进了乳腺病变分割的鲁棒性

Lukas T. Rotkopf, Marco Schlimbach, Julius C. Holzschuh, Heinz-Peter Schlemmer, Jens Kleesiek, Moritz Rempe

机构 * Institute for AI in Medicine (IKIM), University Hospital Essen(人工智能医学研究所(IKIM),埃森大学医院) Department of Physics, Technical University Dortmund(物理系,多特蒙德技术大学) Division of Radiology, German Cancer Research Center (DKFZ)(放射学部,德国癌症研究中心(DKFZ)) Cancer Research Center Cologne Essen (CCCE), University Medicine Essen(科隆埃森癌症研究中心(CCCE),埃森大学医学中心) RACOON Study Group, Site Essen(RACOON研究组,埃森站点) German Cancer Consortium (DKTK), Partner Site Essen(德国癌症联合会(DKTK),埃森合作伙伴站点) Medical Faculty and Faculty of Computer Science, University of Duisburg-Essen(医学系和计算机科学系,杜伊斯堡-埃森大学)

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

AI总结 本文研究了直接从获得的MRI k空间学习乳腺病变分割是否能提高在加速或噪声下的鲁棒性,通过比较不同模型发现基于k空间的深度学习方法在欠采样和噪声下表现更优。

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2605.22031 2026-05-22 cs.CV 90%

SO-Mamba: State-Ownership Mamba for Unrolled MRI Reconstruction

SO-Mamba:用于展开MRI重建的态所有权Mamba

Pengcheng Fang, Hongli Chen, Fangfang Tang, Feng Liu, Xiaohao Cai, Shanshan Shan

机构 * University of Southampton(南安普顿大学) University of Queensland(昆士兰大学) Soochow University(苏州大学)

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

AI总结 本文提出SO-Mamba,一种用于展开MRI重建的态所有权Mamba正则化器,通过分配每个Mamba阶段的重建证据到递归驻留、态接口访问和非态输出校正,以提升重建质量与效率。

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2605.21762 2026-05-22 cs.LG 90%

Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans

利用CT钙扫描中的机会性冠状动脉钙化和心外膜脂肪评估进行阻塞性冠状动脉疾病的机器学习预测

Juhwan Lee, Ammar Hoori, Tao Hu, Justin N. Kim, Mohamed H. E. Makhlouf, Michelle C. Williams, David E. Newby, Robert Gilkeson, Sanjay Rajagopalan, David L. Wilson

机构 * Department of Biomedical Engineering, Virginia Commonwealth University(弗吉尼亚联邦大学生物医学工程系) Department of Biomedical Engineering, Case Western Reserve University(凯斯西储大学生物医学工程系) Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center(克利夫兰医学中心哈灵顿心脏和血管研究所) BHF Centre for Cardiovascular Science, University of Edinburgh(爱丁堡大学BHF心血管科学中心) Department of Radiology, Case Western Reserve University(凯斯西储大学放射学系)

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

AI总结 本研究开发了一种先进的机器学习框架,通过分析CT钙扫描中的冠状动脉钙化和心外膜脂肪数据,预测阻塞性冠状动脉疾病,展示了该方法在提高预测性能和减少对增强CT或侵入性检查依赖方面的潜力。

Comments 16 pages, 4 figures, 3 tables

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2605.20470 2026-05-21 cs.CV cs.AI physics.med-ph 90%

EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis

EPC-3D-Diff: 基于CBCT到CT合成的等价物理一致条件3D潜在扩散模型

Alzahra Altalib, Chunhui Li, Haytham Al Ewaidat, Khaled Alawneh, Ahmad Qendel, Alessandro Perelli

机构 * School of Science and Engineering, University of Dundee UK(邓迪大学科学与工程学院) Faculty of Applied Sciences, Jordan University of Science and Technology(约旦科学技术大学应用科学学院) Experia Healthcare, Jordan(约旦Experia医疗) School of Cardiovascular and Metabolic Health, University of Glasgow UK(格拉斯哥大学心血管与代谢健康学院)

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

AI总结 本文提出EPC-3D-Diff,一种新的条件3D潜在扩散框架,用于体积CBCT到CT合成,通过引入从成像物理导出的投影域等价损失,提高了物理一致性。该方法在训练过程中通过正向投影旋转合成的CT体积,并将其与相应角度偏移的投影进行匹配,从而在扩散目标中集成物理一致的等价约束。

Comments 10 pages, 4 figures

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2501.09799 2026-05-20 eess.IV 90%

Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)

基于邻域优化的扫描自适应MRI欠采样(SUNO)

Siddhant Gautam, Angqi Li, Nicole Seiberlich, Jeffrey A. Fessler, Saiprasad Ravishankar

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

AI总结 本文提出了一种联合学习扫描自适应的Cartesian欠采样模式和相应重建模型的框架,通过交替算法优化欠采样模式和重建模型,利用迭代坐标下降法进行离线优化,并通过最近邻搜索选择测试时的欠采样模式,从而在4×和8×加速因子下提升MRI图像的视觉质量和定量指标。

Comments Published in IEEE Transactions on Computational Imaging, Early Access, Jan. 2026

Journal ref IEEE Transactions on Computational Imaging, pp. 1-13, Jan. 2026

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2605.18466 2026-05-19 cs.CV 90%

Speech-Guided Multimodal Learning for Vocal Tract Segmentation in Real-Time MRI

基于语音引导的多模态学习用于实时MRI中的声道分割

Daiqi Liu, Lukas Mulzer, Md Hasan, Nyvenn de Castro, Fangxu Xing, Xingjian Kang, Chengze Ye, Siyuan Mei, Yipeng Sun, Tomás Arias-Vergara, Jana Hutter, Jonghye Woo, Andreas Maier, Paula Andrea Pérez-Toro

机构 * Harvard Medical School / Massachusetts General Hospital(哈佛医学院/麻省总医院) Institute for Information Processing, Leibniz University Hannover(汉诺威莱布尼茨信息处理研究所) GITA Lab, Facultad de Ingeniería. Universidad de Antioquia UdeA(安提奥基亚大学工程学院GITA实验室)

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

AI总结 本文提出了一种三阶段框架,利用语音和语音学监督进行训练,仅需实时MRI图像进行推理,通过将语音学表示转换为空间边界框先验进行发音器官定位,通过双级跨模态对比预训练对视觉和音频编码器对齐,并通过跨注意力解码器融合学习的表示,有效将多模态知识转移到单模态推理管道中,实验表明该方法在75-Speaker~Annot-16和USC-TIMIT数据集上优于现有单模态和多模态方法。

Comments under review

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2511.13310 2026-05-19 eess.IV physics.med-ph 90%

PyPeT: A Python Perfusion Tool for Automated Quantitative Brain CT and MR Perfusion Analysis

PyPeT:一种用于自动化定量脑CT和MR灌注分析的Python灌注工具

Marijn Borghouts, Ruisheng Su

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

AI总结 该研究提出PyPeT,一种开源的Python灌注工具,用于处理头颅CT灌注和MR灌注数据,生成CBF、CBV、MTT、TTP和Tmax等灌注图,通过统一框架和模块化设计提高灌注研究的可访问性和可定制性。

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2602.12755 2026-05-19 cs.CV 90%

Towards reconstructing experimental sparse-view X-ray CT data with diffusion models

向稀疏视角X射线CT数据重建迈进:基于扩散模型

Nelas J. Thomsen, Xinyuan Wang, Felix Lucka, Ezgi Demircan-Tureyen

机构 * 1 Martin-Luther-University Halle-Wittenberg, Institute of Physics, Halle, Germany 2 Centrum Wiskunde \& Informatica, Computational Imaging Group, Amsterdam, The Netherlands

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

AI总结 本文研究了如何利用扩散模型重建稀疏视角X射线CT数据,探讨了训练数据不匹配(域偏移)和正向模型不匹配对实验数据应用的影响,发现域偏移在不同程度上影响模型性能,而正向模型不匹配可通过退火似然权重调度缓解。

Comments 5 pages + references, 4 figures, 2 tables, conference paper

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2605.17343 2026-05-19 cs.CV 90%

GraphMAR: Geometry-Aware Graph Learning Framework for Spatially Adaptive CT Metal Artifact Reduction

GraphMAR: 一种基于几何的图学习框架用于空间自适应的CT金属伪影减少

Zilong Li, Chenglong Ma, Yiming Lei, Yuanlin Li, Jing Han, Jiannan Liu, Huidong Xie, Junping Zhang, Yi Zhang, Hongming Shan

机构 * Shanghai Key Lab of Intelligent Information Processing, College of Computer Science and Artificial Intelligence, Fudan University(上海智能信息处理关键实验室,计算机科学与人工智能学院,复旦大学) Institute of Science and Technology for Brain-inspired Intelligence, Fudan University(脑启发式智能科学技术研究院,复旦大学) College of Computer Science and Technology, Qingdao University(计算机科学与技术学院,青岛大学) Department of Oral Maxillofacial Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine(口腔颌面头颈肿瘤科,上海第九人民医院,上海交通大学医学院) School of Cyber Science and Engineering, Sichuan University(网络科学与工程学院,四川大学)

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

AI总结 本文提出GraphMAR,一种基于几何的图学习框架,用于在图像域中实现空间自适应的CT金属伪影减少,通过引入图基的几何建模来显式识别伪影并提高恢复质量和可解释性。

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2605.17188 2026-05-19 eess.IV 90%

RDDM: A Residual-Driven Drifting Model for High-Fidelity Low-Dose CT Denoising

RDDM: 一种基于残差驱动的高保真低剂量CT去噪模型

Jianxu Wang, Qing Lyu, Ge Wang

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

AI总结 本文提出RDDM模型,通过引入残差漂移场实现高效且高保真的低剂量CT去噪,解决了传统方法在实时应用中的性能限制,实验表明其在去噪效果和速度上均优于现有方法。

Comments Code is available at: https://github.com/Jayx-Wang/RDDM

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2605.16980 2026-05-19 cs.CV 90%

Statistical Hand Shape Modeling from Clinical CT Scans Using Deep Learning and Implicit Skinning

基于深度学习和隐式皮肤的临床CT扫描中手部形状统计建模

Gokce Guven, Hasan Fehmi Ates, Deniz Karasahin, Kaan Erdogan

机构 * Dept. of Computer Science and Engineering(计算机科学与工程系) Özyeğin University(奥兹耶尼大学) Dept. of Artificial Intelligence and Data Engineering(人工智能与数据工程系) Osteoid Inc.(Osteoid公司)

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

AI总结 本文提出了一种AI辅助的重建流程,利用深度学习和隐式皮肤技术对临床CT扫描中的手部解剖结构进行分割和分析,通过统计形状建模提高生物力学、人机工程学和医疗诊断的应用价值。

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2605.16572 2026-05-19 cs.CV 90%

TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT

TriALS: 三相辅助非增强CT肝脏病变分割基准

Marawan Elbatel, Mohamed Ghonim, Jiaji Mao, Zhuosheng Lin, Katharina Eckstein, Andrés Martínez Mora, Jonathan Deissler, Maximilian Rokuss, Constantin Ulrich, Zdravko Marinov, Wenhui Deng, Baoxun Li, Huijun Hu, Jun Shen, Mohanad Ghonim, Khadiga Omar Nassar, Mariam Elbakry, Menna Dyab, Amr Muhammad Abdo Salem, Nouran Elghitany, Noha Elghitany, Yi Qin, Xuanqi Huang, Haonan Wang, Shao-Woo Yen, Ahmed Elghamry Saba, Salma Ahmad, Xinyan Fang, Jiahao Zhang, Xiaodi Wang, Xinghua Ma, Gongning Luo, Jessica C. Delmoral, João Manuel R. S. Tavares, Ankan Deria, Adinath Dukre, Yutong Xie, Imran Razzak, Dongwook Kim, Matthew Choi, Hanxiao Zhang, Minghui Zhang, Xin You, Abdul Qayyum, Steven A. Niederer, Moona Mazher, Rachika E. Hamadache, Ricardo Montoya-del-Angel, Robert Martí, Xavier Lladó, Toufiq Musah, Livingstone Eli Ayivor, Enrique Almar-Munoz, Agnes Mayr, Kaouther Mouheb, Esther E. Bron, Stefan Klein, Ahmed Abouelhoda, Amira Adel, Susan Adil Ali, Rainer Stiefelhagen, Klaus H. Maier-Hein, Fabian Isensee, Aya Yassin, Xiaomeng Li

机构 * Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology(香港理工大学电子与计算机工程系) AI Center of Excellence, Ain Shams University(爱思明大学人工智能中心) Department of Radiology, Ain Shams University(爱思明大学放射科) Department of Radiology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University(广东省恶性肿瘤表观遗传与基因调控重点实验室,中山大学孙逸仙纪念医院放射科) Nanfang Hospital, Southern Medical University(南方医科大学南华医院) Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany(德国癌症研究中心(DKFZ)医学影像计算部,海德堡,德国) Medical Faculty Heidelberg, Heidelberg University(海德堡大学医学院) Faculty of Mathematics and Computer Science, Heidelberg University(海德堡大学数学与计算机科学学院) Karlsruhe Institute of Technology(卡尔斯鲁厄理工学院)

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

AI总结 本文提出TriALS挑战,通过多中心150例数据评估自动肝脏病变分割算法,在非增强CT条件下取得人类水平性能,但表现受训练数据规模和预训练策略影响显著。

Comments TriALS challenge paper across MICCAI 2024 and 2025; data and code at https://github.com/xmed-lab/TriALS

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2605.15997 2026-05-18 cs.CV 90%

Segmentation, Detection and Explanation: A Unified Framework for CT Appearance Reasoning

分割、检测与解释:一种用于CT外观推理的统一框架

Yuyuan Liu, Can Peng, Yingyu Yang, Qianye Yang, Cheng Ouyang, J. Alison Noble

机构 * Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford(生物医学工程研究所,工程科学系,牛津大学)

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

AI总结 本文提出统一框架,整合语言引导的视觉推理,提升CT图像分割与检测的精度,并提供外观推理输出。

Comments 8 pages, 4 figures, submitted to IEEE Transactions on Medical Imaging (TMI)

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2605.15093 2026-05-18 cs.CV 90%

CoralLite: μCT Reconstruction of Coral Colonies from Individual Corallites

CoralLite: 从个体珊瑚虫中重建珊瑚群体的μCT影像

Jess Jones, Leonardo Bertini, Kenneth Johnson, Erica Hendy, Tilo Burghardt

机构 * University of Bristol(布里斯托大学) University of Liverpool(利物浦大学) Natural History Museum(自然历史博物馆)

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

AI总结 CoralLite通过混合V-Trans-UNet架构实现对珊瑚骨骼的3D重建,首次展示视觉机器学习在单个珊瑚骨骼μCT扫描中对个体珊瑚虫的完整建模能力。

Comments 15 pages, 10 figures, 2 tables

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