医学 AI

Interpreting Biomedical VLMs on High-Imbalance Out-of-Distributions: An Insight into BiomedCLIP on Radiology

Nafiz Sadman, Farhana Zulkernine, Benjamin Kwan

机构 * School of Computing（计算学院） ； Department of Diagnostic Radiology（诊断放射科） ； Queen's University（皇后大学） ； Kingston Health Sciences Centre（金斯顿健康科学中心） 纠错

专题命中 医学影像 ：radiology(title)；biomedical(title)；分类 cs.CV

Comments GitHub: https://github.com/Nafiz95/BioVLM_Eval_CXR

MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation

Fenghe Tang, Bingkun Nian, Yingtai Li, Zihang Jiang, Jie Yang, Wei Liu, S. Kevin Zhou

机构 * School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（科学技术大学生物医学工程学院） ； Suzhou Institute for Advanced Research, University of Science and Technology of China（科学技术大学苏州市先进研究所） ； Center for Medical Imaging, Robotics, and Analytic Computing & LEarning (MIRACLE), Suzhou Institute for Advanced Research, USTC（医学影像、机器人与分析计算与学习中心） ； Department of Automation, Shanghai Jiao Tong University（上海交通大学自动化系） ； Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University（上海交通大学图像处理与模式识别研究所） ； Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医学机器人研究所） ； State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China（科学技术大学精密与智能化学国家重点实验室） 纠错

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

Comments Accepted by Medical Image Analysis. Code: https://github.com/FengheTan9/MambaMIM

Journal ref Medical Image Analysis, Volume 103, 2025, Article 103606

Zeus: Zero-shot LLM Instruction for Union Segmentation in Multimodal Medical Imaging

Siyuan Dai, Kai Ye, Guodong Liu, Haoteng Tang, Liang Zhan

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

Comments 21 pages, 4 figures, In Press by a journal

iMedImage Technical Report

Ran Wei, ZhiXiong Lan, Qing Yan, Ning Song, Ming Lv, LongQing Ye

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

Conformal confidence sets for biomedical image segmentation

Samuel Davenport

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

2D and 3D Deep Learning Models for MRI-based Parkinson's Disease Classification: A Comparative Analysis of Convolutional Kolmogorov-Arnold Networks, Convolutional Neural Networks, and Graph Convolutional Networks

Salil B Patel, Vicky Goh, James F FitzGerald, Chrystalina A Antoniades

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

Comments 7 figures

Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images

Jason Walsh, Alice Othmani, Mayank Jain, Soumyabrata Dev

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

Comments Published in Healthcare Analytics, 2022

Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology

Pranav Agnihotri, Sara Ketabi, Khashayar, Namdar, Farzad Khalvati

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

Detecting Schizophrenia with 3D Structural Brain MRI Using Deep Learning

Junhao Zhang, Vishwanatha M. Rao, Ye Tian, Yanting Yang, Nicolas Acosta, Zihan Wan, Pin-Yu Lee, Chloe Zhang, Lawrence S. Kegeles, Scott A. Small, Jia Guo

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

Comments 13 pages, 6 figures

Meta-data Study in Autism Spectrum Disorder Classification Based on Structural MRI

Ruimin Ma, Yanlin Wang, Yanjie Wei, Yi Pan

专题命中 医学影像 ：MRI(title,abstract)；diagnosis(abstract)；分类 cs.LG、q-bio、eess.IV

A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation

Zhenyu Yang, Zongsheng Hu, Hangjie Ji, Kyle Lafata, Scott Floyd, Fang-Fang Yin, Chunhao Wang

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

Comments 30 pages, 7 figures, 2 tables

BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation

Yanda Meng, Hongrun Zhang, Dongxu Gao, Yitian Zhao, Xiaoyun Yang, Xuesheng Qian, Xiaowei Huang, Yalin Zheng

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

Comments Accepted in BMVC2021 as Oral

Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Data

Nanyan Zhu, Chen Liu, Xinyang Feng, Dipika Sikka, Sabrina Gjerswold-Selleck, Scott A. Small, Jia Guo

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

Comments Published in IEEE ISBI 2021. Available at https://ieeexplore.ieee.org/document/9433808

A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network

Weiwei Zong, Joon Lee, Chang Liu, Eric Carver, Aharon Feldman, Branislava Janic, Mohamed Elshaikh, Milan Pantelic, David Hearshen, Indrin Chetty, Benjamin Movsas, Ning Wen

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

Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

Fei Shan, Yaozong Gao, Jun Wang, Weiya Shi, Nannan Shi, Miaofei Han, Zhong Xue, Dinggang Shen, Yuxin Shi

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

Comments 23 pages, 6 figures

Smile-GANs: Semi-supervised clustering via GANs for dissecting brain disease heterogeneity from medical images

Zhijian Yang, Junhao Wen, Christos Davatzikos

专题命中 医学影像 ：medical image(title)；MRI(abstract)；biomedical(abstract)；分类 cs.LG、q-bio、eess.IV

Knee Cartilage Segmentation Using Diffusion-Weighted MRI

Alejandra Duarte, Chaitra V. Hegde, Aakash Kaku, Sreyas Mohan, José G. Raya

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

Comments Accepted to Medical Imaging Meets NeurIPS 2019

Knowledge-based Analysis for Mortality Prediction from CT Images

Hengtao Guo, Uwe Kruger, Ge Wang, Mannudeep K. Kalra, Pingkun Yan

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

Comments Accepted for publication in IEEE Journal of Biomedical and Health Informatics (JBHI)

Journal ref IEEE Journal of Biomedical and Health Informatics, 2019

Dental pathology detection in 3D cone-beam CT

Adel Zakirov, Matvey Ezhov, Maxim Gusarev, Vladimir Alexandrovsky, Evgeny Shumilov

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

maskSLIC: Regional Superpixel Generation with Application to Local Pathology Characterisation in Medical Images

Benjamin Irving

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

Comments The article has been submitted to IEEE TPAMI

Adaptive Local Window for Level Set Segmentation of CT and MRI Liver Lesions

Assaf Hoogi, Christopher F. Beaulieu, Guilherme M. Cunha, Elhamy Heba, Claude B. Sirlin, Sandy Napel, Daniel L. Rubin

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

Comments 24 pages, 11 figures, 3 tables

U-Net: Convolutional Networks for Biomedical Image Segmentation

Olaf Ronneberger, Philipp Fischer, Thomas Brox

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

Comments conditionally accepted at MICCAI 2015

An Automatic Seeded Region Growing for 2D Biomedical Image Segmentation

Mohammed M. Abdelsamea

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

Comments appears in Proceedings of International Conference on Environment and Bio-Science 2011. subset of arXiv:1407.3664

Adaptive Mesh Representation and Restoration of Biomedical Images

Ke Liu, Ming Xu, Zeyun Yu

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

ReclAIm: A Multi-Agent Framework for Monitoring and Correcting Performance Decline in Medical Imaging AI

ReclAIm：用于监测和纠正医学影像AI性能下降的多智能体框架

Eleftherios Tzanis, Michail E. Klontzas

机构 * 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实验室，放射科，医学院，希腊克里特大学） ； Computational Biomedicine Laboratory, Institute of Computer Science Foundation for Research and Technology Hellas (ICS - FORTH), Heraklion, Crete, Greece（计算生物医学实验室，希腊基础研究与技术院计算机科学研究所（ICS - FORTH），克里特，希腊） ； Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute, Huddinge, Sweden（放射科，临床科学、干预与技术部（CLINTEC），卡罗林斯卡研究所，瑞典Huddinge） 纠错

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

AI总结 提出基于大语言模型的多智能体框架ReclAIm，通过自然语言交互自动监测医学图像分类模型性能下降并触发微调，采用数据增强、类别不平衡处理和参数锚定正则化策略，在多个数据集上验证了有效性。

Comments Published in Radiology: Artificial Intelligence (https://doi.org/10.1148/ryai.250923)

Towards Segmenting the Invisible: An End-to-End Registration and Segmentation Framework for Weakly Supervised Tumour Analysis

朝着不可见的分割迈进：一种端到端的配准和分割框架用于弱监督肿瘤分析

Budhaditya Mukhopadhyay, Chirag Mandal, Pavan Tummala, Naghmeh Mahmoodian, Andreas Nürnberger, Soumick Chatterjee

机构 * Institute of Technical ； Business Information Systems, Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany Human Technopole, Milan, Italy Institute of Medical Engineering, Faculty of Electrical Engineering ； Information Technology, Otto von Guericke University Magdeburg, Magdeburg, Germany Centre for Behavioural Brain Sciences, Magdeburg, Germany 纠错

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

AI总结 本文提出一种端到端配准和分割框架，用于弱监督肿瘤分析，通过跨模态配准生成伪标签，但发现无法有效分割不可见病理学。

Comments Accepted for AIBio at ECAI 2025

Journal ref Artificial Intelligence for Biomedical Data, AIBIO 2025, CCIS 2696, pp 229-242, 2026

Comprehensive Validation of Automated Whole Body Skeletal Muscle, Adipose Tissue, and Bone Segmentation from 3D CT images for Body Composition Analysis: Towards Extended Body Composition

Da Ma, Vincent Chow, Karteek Popuri, Mirza Faisal Beg

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

Comments This paper is based on concepts presented at the NIH Body Composition and Cancer Outcomes Research Webinar Series on December 17th, 2020 by Mirza Faisal Beg titled "Automating Body Composition from Routinely Acquired CT images - towards 3D measurements". The talk is archived [here](https://epi.grants.cancer.gov/events/body-composition/#past)

CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration

CoM³eT：通过联邦多维上下文集成实现医学图像分析的基础模型

J. Raphael Schäfer, Kai Geissler, Till Nicke, Chiara Tappermann, Karoline Heber, Eike Petersen, Habib Mergan, Lars Ole Schwen, Nick Weiss, Annika Gerken, Jan Hendrik Moltz, Tom Bisson, Isil Dogan O, Tim-Rasmus Kiehl, Norman Zerbe, Sefer Elezkurtaj, Robin S. Mayer, Nadine Flinner, Peter Wild, Isabel Dahm, Felix Peisen, Heinrich von Busch, Robert Grimm, Sebastian Arndt, Lisa Siegler, Matthias Stefan May, Antje Prasse, Natalia Artysh, Fabian Kiessling, Johannes Lotz

机构 * Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所） ； RWTH Aachen University（亚琛工业大学） ； Medizinische Hochschule Hannover（汉诺威医学院） ； Massachusetts General Hospital（麻省总医院） ； Harvard Medical School（哈佛医学院） ； Charité – Universitätsmedizin Berlin（柏林夏里特医学院） ； Freie Universität Berlin（柏林自由大学） ； Humboldt-Universität zu Berlin（柏林洪堡大学） 纠错

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

AI总结 CoM³eT是统一多专科、多预测类型及多维度输入的医学视觉基础模型，在公开竞赛中表现优于同类模型，仅微调少量参数即可适配多临床任务，联邦学习场景下性能接近聚合数据训练。

Physics-Informed Implicit Neural Representations for Improved Myocardial Perfusion MRI Quantification

用于改进心肌灌注MRI定量的物理信息隐式神经表示

Christos Tsepas, Chang Yan, Maximilian Fuetterer, Sebastian Kozerke, Cian M Scannell

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

AI总结 该研究将物理信息神经网络（PINN）框架扩展加入时空隐式神经表示（INRs），在真实模拟CMR数据集上提升了心肌灌注参数估计的鲁棒性与准确性。

Comments Accepted at the STACOM workshop at MICCAI, Strasbourg 2026

RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding

RadPRISM：用于概念解耦图像表示与视觉定位的模式分层放射学报告监督方法

Fabian Drexel, Marlene Fritzsche, Era Stambollxhiu, Miriam Kumpf, Lena Schmitzer, Lea Schumann, Jannik Kahmann, Friedrich Puttkammer, Johannes Moll, Jannik Lübberstedt, Zeineb Ben Chaaben, Anirudh Narayanan, Cosmin I. Bercea, Sebastian Ziegelmayer, Marcus R. Makowski, Daniel Rueckert, Lisa C. Adams, Keno K. Bressem

机构 * Technical University of Munich (TUM)（慕尼黑工业大学（TUM）） ； TUM University Hospital（慕尼黑工业大学医院） ； Technical University of Munich, School of Medicine and Health（慕尼黑工业大学医学与健康学院） ； Klinikum rechts der Isar（右伊萨尔医院） ； Charité – Universitätsmedizin Berlin（柏林夏里特医学院） ； Freie Universität Berlin（柏林自由大学） ； Humboldt Universität zu Berlin（柏林洪堡大学） ； Imperial College London（伦敦帝国理工学院） ； Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML）） ； University Hospital Essen (AöR)（埃森大学医院（AöR）） ； Institute for Artificial Intelligence in Medicine (IKIM)（医学人工智能研究所（IKIM）） ； Institute of Interventional and Diagnostic Radiology and Neuroradiology（介入与诊断放射学及神经放射学研究所） ； National Center for Tumor Diseases West（西部肿瘤疾病国家中心） 纠错

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

AI总结 RadPRISM将放射学模式作为分层轴，通过专用视觉子空间对齐临床概念，提升零样本分类与视觉定位性能，实现可透明检查的概念解耦医学图像表示。