医学 AI

WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, Shaoting Zhang

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

Comments Accepted to Medical Image Analysis, dataset at: https://github.com/HiLab-git/WORD (we corrected the results or description in this version.)

The role of noise in denoising models for anomaly detection in medical images

Antanas Kascenas, Pedro Sanchez, Patrick Schrempf, Chaoyang Wang, William Clackett, Shadia S. Mikhael, Jeremy P. Voisey, Keith Goatman, Alexander Weir, Nicolas Pugeault, Sotirios A. Tsaftaris, Alison Q. O'Neil

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

Comments Submitted to Medical Image Analysis special issue for MIDL 2022

InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images

Hong Wang, Yuexiang Li, Haimiao Zhang, Deyu Meng, Yefeng Zheng

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

Journal ref Medical Image Analysis 2022

Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis

Shishuai Hu, Zehui Liao, Yong Xia

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

Comments Technical report. Solution to CarOtid vessel wall SegMentation and atherosclerOsis diagnosiS challenge (COSMOS 2022)

Lung nodules segmentation from CT with DeepHealth toolkit

Hafiza Ayesha Hoor Chaudhry, Riccardo Renzulli, Daniele Perlo, Francesca Santinelli, Stefano Tibaldi, Carmen Cristiano, Marco Grosso, Attilio Fiandrotti, Maurizio Lucenteforte, Davide Cavagnino

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

Comments Workshop ICIAP 2021 - Deep-Learning and High Performance Computing to Boost Biomedical Applications

Recent advances and clinical applications of deep learning in medical image analysis

Xuxin Chen, Ximin Wang, Ke Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu

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

Comments To appear in the journal Medical Image Analysis. The registration section was revised

Towards Super-Resolution CEST MRI for Visualization of Small Structures

Lukas Folle, Katharian Tkotz, Fasil Gadjimuradov, Lorenz Kapsner, Moritz Fabian, Sebastian Bickelhaupt, David Simon, Arnd Kleyer, Gerhard Krönke, Moritz Zaiß, Armin Nagel, Andreas Maier

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

Journal ref Proceedings, German Workshop on Medical Image Computing (2022) 210-215

Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels

Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer

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

Comments Accepted in Medical Image Analysis

Boundary-aware Context Neural Network for Medical Image Segmentation

Ruxin Wang, Shuyuan Chen, Chaojie Ji, Jianping Fan, Ye Li

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

Journal ref Medical Image Analysis, 2022

Semi-automated Virtual Unfolded View Generation Method of Stomach from CT Volumes

Masahiro Oda, Tomoaki Suito, Yuichiro Hayashi, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Gen Iinuma, Kazunari Misawa, Shigeru Nawano, Kensaku Mori

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

Comments Accepted paper as a poster presentation at MICCAI 2013 (International Conference on Medical Image Computing and Computer-Assisted Intervention), Nagoya, Japan

Journal ref Published in Proceedings of MICCAI 2013, LNCS 8149, pp.332-339, 2013

3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI

Marcel Bengs, Finn Behrendt, Julia Krüger, Roland Opfer, Alexander Schlaefer

专题命中 医学影像 ：MRI(title,abstract)；diagnosis(abstract)；分类 cs.CV、eess.IV；radiology(comments)

Comments Accepted for publication in the International Journal of Computer Assisted Radiology and Surgery (IJCARS)

Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network

Xiao Qi, Lloyd Brown, David J. Foran, Ilker Hacihaliloglu

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

Comments 16 pages, 9 figures

Journal ref International Journal of Computer Assisted Radiology and Surgery, 2021

CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Compartments Segmentation on CT Images

Song Wang, Yuting He, Youyong Kong, Xiaomei Zhu, Shaobo Zhang, Pengfei Shao, Jean-Louis Dillenseger, Jean-Louis Coatrieux, Shuo Li, Guanyu Yang

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

Comments 24th International Conference on Medical Image Computing and Computer Assisted Intervention

Is it Time to Replace CNNs with Transformers for Medical Images?

Christos Matsoukas, Johan Fredin Haslum, Magnus Söderberg, Kevin Smith

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

Comments Originally published at the ICCV 2021 Workshop on Computer Vision for Automated Medical Diagnosis (CVAMD)

Rigid and non-rigid motion compensation in weight-bearing cone-beam CT of the knee using (noisy) inertial measurements

Jennifer Maier, Marlies Nitschke, Jang-Hwan Choi, Garry Gold, Rebecca Fahrig, Bjoern M. Eskofier, Andreas Maier

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

Comments 16 pages, 6 figures, submitted to Elsevier Medical Image Analysis on Feb 11, 2021

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis

Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu

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

Comments 27 pages, 18 figures

Journal ref Medical Image Analysis 2021

Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan

Dong Yang, Ziyue Xu, Wenqi Li, Andriy Myronenko, Holger R. Roth, Stephanie Harmon, Sheng Xu, Baris Turkbey, Evrim Turkbey, Xiaosong Wang, Wentao Zhu, Gianpaolo Carrafiello, Francesca Patella, Maurizio Cariati, Hirofumi Obinata, Hitoshi Mori, Kaku Tamura, Peng An, Bradford J. Wood, Daguang Xu

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

Comments Accepted with minor revision to Medical Image Analysis

Organ Segmentation From Full-size CT Images Using Memory-Efficient FCN

Chenglong Wang, Masahiro Oda, Kensaku Mori

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

Journal ref Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140I

MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better

Yuhua Chen, Anthony G. Christodoulou, Zhengwei Zhou, Feng Shi, Yibin Xie, Debiao Li

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

Comments Preprint submitted to Medical Image Analysis

Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks

Gihyun Kwon, Chihye Han, Dae-shik Kim

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

Comments 8.5 pages, 4 figures, Accepted by the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019)

Quantitative Error Prediction of Medical Image Registration using Regression Forests

Hessam Sokooti, Gorkem Saygili, Ben Glocker, Boudewijn P. F. Lelieveldt, Marius Staring

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

Journal ref Medical Image Analysis, 2019, ISSN 1361-8415

Cats or CAT scans: transfer learning from natural or medical image source datasets?

Veronika Cheplygina

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

Comments Accepted to Current Opinion in Biomedical Engineering

Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review

无监督深度生成模型在神经影像中的异常检测：一项系统性综述

Youwan Mahé, Elise Bannier, Stéphanie Leplaideur, Elisa Fromont, Francesca Galassi

机构 * Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn, Rennes, France（法国里昂大学、Inria、CNRS、Inserm、IRISA UMR 6074、Empenn、里昂） ； Siemens Healthineers, Courbevoie, France（法国西门子医疗影像公司、Courbevoie） ； CHU Rennes, Radiology Department, Rennes, France（里昂大学医院、放射科、法国里昂） ； CHU Rennes, Physical Medicine and Rehabilitation Department, Rennes, France（里昂大学医院、康复医学部、法国里昂） ； Centre de Kerpape, Ploemeur, France（凯帕尔中心、法国普洛梅尔） ； Univ Rennes, Inria, CNRS, IRISA, Rennes, France（法国里昂大学、Inria、CNRS、IRISA、里昂） 纠错

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

AI总结 本文综述了无监督深度生成模型在神经影像异常检测中的应用，指出其在病理无关定位中的潜力及现存挑战。

MedPlex: Deep Vision-Language Co-Adaptation for Clinically Grounded Medical Segmentation

MedPlex：用于临床基础医学分割的深度视觉-语言协同适配

Rafi Ibn Sultan, Hui Zhu, Chengyin Li, Dongxiao Zhu

机构 * Wayne State University（韦恩州立大学） ； Henry Ford Health（亨利福特医疗集团） ； Institute for AI and Data Science Wayne State University（韦恩州立大学人工智能与数据科学研究院） 纠错

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

AI总结 MedPlex是一种端到端VLM框架，通过双向融合和两级概念对齐，实现医学图像分割的视觉-语言协同适配，在CT、MR的多类医学分割任务中达到最优性能。

Comments Accepted By BMVC-2026

Foundation Models in Biomedical Imaging: Turning Hype into Reality

生物医学影像中的基础模型：从 hype 到现实

Amgad Muneer, Kai Zhang, Ibraheem Hamdi, Rizwan Qureshi, Muhammad Waqas, Shereen Fouad, Hazrat Ali, Syed Muhammad Anwar, Jia Wu

机构 * Department of Imaging Physics, The University of Texas MD Anderson Cancer Center（影像物理系，德克萨斯大学MD安德森癌症中心） ； Center for Secure Artificial Intelligence for Healthcare (SAFE), McWilliams School of Biomedical Informatics, UTHealth Houston（安全人工智能用于医疗保健中心（SAFE），麦威廉斯生物医学信息学学院，UTHealth休斯顿） ； Female Medicine in Machine Learning, Massachusetts Institute of Technology（机器学习中的女性医学，麻省理工学院） ； Department of Computer Science, Salim Habib University（计算机科学系，Salim Habib大学） ； School of Computer Science and Digital Technologies, Aston Centre for Artificial Intelligence Research and Application, Aston University（计算机科学与数字技术学院，阿斯顿人工智能研究与应用中心，阿斯顿大学） ； Division of Computing Science and Mathematics, University of Stirling（计算科学与数学系，斯特灵大学） ； School of Medicine and Health Sciences, George Washington University（医学与健康科学学院，乔治·华盛顿大学） ； Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital（谢赫扎耶德儿童外科创新研究所，儿童医院） ； Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center（胸腔/头颈医学肿瘤科，德克萨斯大学MD安德森癌症中心） 纠错

专题命中 医学影像 ：biomedical(title,abstract)；pathology(abstract)；分类 cs.CV、cs.LG、q-bio

AI总结 本文探讨了基础模型在生物医学影像中的应用，提出REAL-FM框架以评估模型的实际临床价值，指出基础模型在因果推理和安全性方面存在不足，强调需要协调的专业AI系统。

Comments 9 figures and 3 tables

Journal ref Nature Biomedical Engineering 10, 1557-1575 (2026)

HounsWorld: A Multimodal World Model for Hidden Patient-State Readout, Reconstruction, and Simulation

HounsWorld：用于隐藏患者状态读取、重建与模拟的多模态世界模型

Yunhao Bai, Zhongwei Qiu, Guangyu Guo, Yiming Huang, Tony C. W. Mok, Qinji Yu, Ling Zhang, Yan Wang

机构 * East China Normal University（华东师范大学） ； DAMO Academy, Alibaba Group（阿里巴巴集团达摩院） ； Hupan Laboratory（湖畔实验室） ； Zhejiang University（浙江大学） 纠错

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

AI总结 该研究提出3B参数多模态世界模型HounsWorld，结合CT扫描与临床语言，通过共享潜在患者状态实现读取、重建、模拟三类任务，在HounsBench基准上表现优异，提升了CT理解能力。

Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matching

基于全容积多任务潜在流匹配的组合跨模态医学图像转换

Daniele Molino, Alessio Zoboli, Camillo Maria Caruso, Valerio Guarrasi, Paolo Soda

机构 * Università Campus Bio-Medico di Roma（罗马 Campus Bio-Medico 大学） ； Umeå University（于默奥大学） ； UniCamillus – Saint Camillus International University of Health Sciences（UniCamillus – 圣卡米勒斯国际健康科学大学） 纠错

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 该研究提出基于全容积多任务潜在流匹配的方法，解耦容积先验与跨模态映射目标，训练单个多任务模型实现跨/模态内转换，在全容积处理、零样本泛化等方面优于基线，为合成系统泛化提供可扩展途径。

Comments Accepted ad Sashimi 2026

UltraSAM3: A Concept-Driven Foundation Model for Universal Ultrasound Image Segmentation

UltraSAM3：一种用于通用超声图像分割的概念驱动基础模型

Bo Xu, Quanhao Zhu, Rui Lin, Boling Zhu, Chenyuan Wang, Hongfei Lin, Feng Xia, Chenhua Ji

机构 * Dalian University of Technology Affiliated Center Hospital（大连大学附属中心医院） 纠错

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

AI总结 针对现有超声分割方法的任务局限或需专家视觉提示问题，提出概念驱动的UltraSAM3模型及指令引导智能体，在多基准测试中性能优于同类模型，提升了临床交互的实用性。

RadHarmony: Radiological Data Handling in the Era of Agentic AI

RadHarmony：智能体AI时代的放射数据处理方案

Frank Li, Bardia Khosravi, Mohammadreza Chavoshi, Theo Dapamede, YoungSeok Jeon, Janice Newsome, Hari Trivedi, Judy Gichoya

机构 * Emory University（埃默里大学） ； Yale University（耶鲁大学） 纠错

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

AI总结 RadHarmony是一款开源Python库，用于统一处理异构放射数据集，引入AI智能体技能简化数据集整合，可预训练视觉Transformer基线并提供相关代码与模型权重。

Comparing the Performance of Foundation Model Derived Embeddings with Traditional Approaches for Distant Metastasis Prediction in Head and Neck Cancer

比较基于基础模型的嵌入与传统方法在头颈部癌远处转移预测中的性能

Erich Schmitz, Meixu Chen, Bowen Jing, Jing Wang

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

AI总结 本研究以RADCURE数据集2327例HNC患者术前CT图像为基础，对比CT基础模型嵌入、放射组学等特征集预测远处转移的性能，发现CT基础模型嵌入性能更优，可作为传统放射组学的替代方案

Comments 27 pages including supplemental materials, 5 main figures, 2 supplemental figures, 5 main tables, 7 supplemental tables. Poster Abstract at 2026 AAPM Meeting and Exhibition