医学 AI

Gigapixel microscopy using a flatbed scanner

Guoan Zheng, Xiaoze Ou, Changhuei Yang

专题命中 病理影像 ：pathology(abstract)；biomedical(abstract)

Breast Cancer VLMs: Clinically Practical Vision-Language Train-Inference Models

Shunjie-Fabian Zheng, Hyeonjun Lee, Thijs Kooi, Ali Diba

机构 * Department of Medicine I, LMU University Hospital, LMU Munich, Germany（慕尼黑大学医学院第一医学部，LMU慕尼黑大学医院） ； Lunit Inc.（Lunit公司） 纠错

专题命中 病理影像 ：diagnosis(abstract,comments)；分类 cs.CV、cs.LG

Comments Accepted to Computer Vision for Automated Medical Diagnosis (CVAMD) Workshop at ICCV 2025

Transformer-Based Explainable Deep Learning for Breast Cancer Detection in Mammography: The MammoFormer Framework

Ojonugwa Oluwafemi Ejiga Peter, Daniel Emakporuena, Bamidele Dayo Tunde, Maryam Abdulkarim, Abdullahi Bn Umar

专题命中 病理影像 ：medical image(abstract)；分类 cs.CV、eess.IV；biomedical(journal_ref)

Journal ref Proc. SPIE 13410, Medical Imaging 2025: Clinical and Biomedical Imaging, 1341024 (2 April 2025)

Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer

Souradeep Chakraborty, Ruoyu Xue, Rajarsi Gupta, Oksana Yaskiv, Constantin Friedman, Natallia Sheuka, Dana Perez, Paul Friedman, Won-Tak Choi, Waqas Mahmud, Beatrice Knudsen, Gregory Zelinsky, Joel Saltz, Dimitris Samaras

机构 * Department of Computer Science（计算机科学系） ； Department of Biomedical Informatics（生物医学信息学系） ； Department of Psychology（心理学系） ； Department of Pathology and Laboratory Medicine（病理学与实验室医学系） ； Department of Pathology（病理学系） 纠错

专题命中 病理影像 ：pathology(abstract)；分类 cs.CV、eess.IV；medical image(comments)

Comments Accepted to Medical Image Analysis (MEDIA), Elsevier, 2025. This is the accepted manuscript version; the final published article link will be updated when available

Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study

Sania Eskandari, Ali Eslamian, Nusrat Munia, Amjad Alqarni, Qiang Cheng

机构 * University of Kentucky（肯塔基大学） ； Institute for Biomedical Informatics（生物医学信息学研究所） 纠错

专题命中 病理影像 ：diagnosis(abstract)；分类 cs.CV、eess.IV；pathology(journal_ref)

Comments 4 pages, 2 figures, 2 tables

Journal ref In Medical Imaging 2025: Digital and Computational Pathology (Vol. 13413, pp. 289-294). SPIE

Z-Stack Scanning can Improve AI Detection of Mitosis: A Case Study of Meningiomas

Hongyan Gu, Ellie Onstott, Wenzhong Yan, Tengyou Xu, Ruolin Wang, Zida Wu, Xiang 'Anthony' Chen, Mohammad Haeri

专题命中 病理影像 ：pathology(abstract)；分类 cs.CV、eess.IV；biomedical(comments)

Comments To appear 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)

Stain-Invariant Representation for Tissue Classification in Histology Images

Manahil Raza, Saad Bashir, Talha Qaiser, Nasir Rajpoot

专题命中 病理影像 ：pathology(abstract)；分类 cs.CV、cs.LG；medical image(journal_ref)

Journal ref In 27th Conference on Medical Image Understanding and Analysis 2023 (p. 242)

Analysis and Validation of Image Search Engines in Histopathology

Isaiah Lahr, Saghir Alfasly, Peyman Nejat, Jibran Khan, Luke Kottom, Vaishnavi Kumbhar, Areej Alsaafin, Abubakr Shafique, Sobhan Hemati, Ghazal Alabtah, Nneka Comfere, Dennis Murphee, Aaron Mangold, Saba Yasir, Chady Meroueh, Lisa Boardman, Vijay H. Shah, Joaquin J. Garcia, H. R. Tizhoosh

专题命中 病理影像 ：diagnosis(abstract)；分类 cs.CV、eess.IV；biomedical(journal_ref)

Journal ref IEEE Reviews in Biomedical Engineering, 2024

Integrating Preprocessing Methods and Convolutional Neural Networks for Effective Tumor Detection in Medical Imaging

Ha Anh Vu

专题命中 病理影像 ：medical image(abstract)；分类 cs.CV、eess.IV；MRI(comments)

Comments 5 pages, 5 figures, utilizing convolutional neural networks and preprocessing methods for tumor detection in MRI images, featuring a detailed methodology section on image preprocessing, segmentation, and model training, with a comprehensive evaluation of model performance on the Figshare dataset using IEEE template

A Spatiotemporal Illumination Model for 3D Image Fusion in Optical Coherence Tomography

Stefan Ploner, Jungeun Won, Julia Schottenhamml, Jessica Girgis, Kenneth Lam, Nadia Waheed, James Fujimoto, Andreas Maier

专题命中 病理影像 ：pathology(abstract)；分类 cs.CV、eess.IV；biomedical(comments)

Comments Presented orally & as poster on 20th April 2023 at the IEEE International Symposium on Biomedical Imaging (ISBI) in Cartagena, Colombia. 6 pages, 3 figures. You can find the official version with broken equations and bad contrast figures under https://ieeexplore.ieee.org/document/10230526

Unleashing the Infinity Power of Geometry: A Novel Geometry-Aware Transformer (GOAT) for Whole Slide Histopathology Image Analysis

Mingxin Liu, Yunzan Liu, Pengbo Xu, Jiquan Ma

专题命中 病理影像 ：diagnosis(abstract)；分类 cs.CV、eess.IV；biomedical(comments)

Comments 5 pages, 3 figures. Accepted by 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024)

AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images

Pei Liu, Luping Ji, Feng Ye, Bo Fu

专题命中 病理影像 ：pathology(abstract)；分类 cs.CV、eess.IV；medical image(journal_ref)

Comments 15 pages, 10 figures, 8 tables

Journal ref Medical Image Analysis, 103020 (2023)

LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening

Beidi Zhao, Wenlong Deng, Zi Han, Li, Chen Zhou, Zuhua Gao, Gang Wang, Xiaoxiao Li

专题命中 病理影像 ：pathology(abstract)；分类 cs.CV、eess.IV；medical image(comments)

Comments This paper was submitted to Medical Image Analysis. It is under review

Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy

Kasra Arnavaz, Oswin Krause, Kilian Zepf, Jelena M. Krivokapic, Silja Heilmann, Jakob Andreas Bærentzen, Pia Nyeng, Aasa Feragen

专题命中 病理影像 ：biomedical(abstract,comments)；分类 cs.CV、cs.LG

Comments Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.org/papers/2022:015.html"

Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels

Ahmet Gokberk Gul, Oezdemir Cetin, Christoph Reich, Tim Prangemeier, Nadine Flinner, Heinz Koeppl

专题命中 病理影像 ：diagnosis(abstract)；分类 cs.CV、cs.LG；pathology(journal_ref)

Journal ref Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120391O (4 April 2022)

A Deep Learning Study on Osteosarcoma Detection from Histological Images

D M Anisuzzaman, Hosein Barzekar, Ling Tong, Jake Luo, Zeyun Yu

专题命中 病理影像 ：diagnosis(abstract)；分类 cs.CV、eess.IV；biomedical(journal_ref)

Journal ref Biomedical Signal Processing and Control 69 (2021): 102931

Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)

Varun Mannam, Yide Zhang, Xiaotong Yuan, Takashi Hato, Pierre C. Dagher, Evan L. Nichols, Cody J. Smith, Kenneth W. Dunn, Scott Howard

专题命中 病理影像 ：biomedical(abstract,comments)；分类 cs.CV、eess.IV

Comments SPIE Proceedings Volume 11648, Multiphoton Microscopy in the Biomedical Sciences XXI; 116481C (2021)

Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images

Zhe Min, Fernando J. Bianco, Qianye Yang, Rachael Rodell, Wen Yan, Dean Barratt, Yipeng Hu

专题命中 病理影像 ：diagnosis(abstract)；分类 cs.CV、eess.IV；medical image(comments)

Comments Accepted by 25th UK Conference on Medical Image Understanding and Analysis(MIUA 2021)

Cell abundance aware deep learning for cell detection on highly imbalanced pathological data

Yeman Brhane Hagos, Catherine SY Lecat, Dominic Patel, Lydia Lee, Thien-An Tran, Manuel Rodriguez- Justo, Kwee Yong, Yinyin Yuan

专题命中 病理影像 ：pathology(abstract)；分类 cs.CV、eess.IV；biomedical(comments)

Comments Accepted at The IEEE International Symposium on Biomedical Imaging (ISBI) 2021, 5 pages, 5 figures

Gastric histopathology image segmentation using a hierarchical conditional random field

Changhao Sun, Chen Li, Jinghua Zhang, Muhammad Rahaman, Shiliang Ai, Hao Chen, Frank Kulwa, Yixin Li, Xiaoyan Li, Tao Jiang

专题命中 病理影像 ：diagnosis(abstract)；分类 cs.CV、cs.LG；biomedical(journal_ref)

Journal ref Biocybernetics and Biomedical Engineering, 2020, 40(4): 1535-1555

Virtual staining for mitosis detection in Breast Histopathology

Caner Mercan, Germonda Reijnen-Mooij, David Tellez Martin, Johannes Lotz, Nick Weiss, Marcel van Gerven, Francesco Ciompi

专题命中 病理影像 ：diagnosis(abstract)；分类 cs.CV、eess.IV；biomedical(comments)

Comments 5 pages, 4 figures. Accepted for publication at the IEEE International Symposium on Biomedical Imaging (ISBI), 2020

Patch Clustering for Representation of Histopathology Images

Wafa Chenni, Habib Herbi, Morteza Babaie, H. R. Tizhoosh

专题命中 病理影像 ：medical image(abstract)；分类 cs.CV、eess.IV；pathology(comments)

Comments Accepted for publication in the 15th European Congress on Digital Pathology (ECDP 2019), University of Warwick, UK

Deep Features for Tissue-Fold Detection in Histopathology Images

Morteza Babaie, H. R. Tizhoosh

专题命中 病理影像 ：diagnosis(abstract)；分类 cs.CV、eess.IV；pathology(comments)

Comments Accepted for publication in the 15th European Congress on Digital Pathology (ECDP 2019), University of Warwick, UK

Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction

基于全切片图像的基因表达预测的深度回归模型的评估与预后验证

Fredrik K. Gustafsson, Constance Boissin, Johan Vallon-Christersson, Mattias Rantalainen

专题命中 病理影像 ：pathology(abstract)；分类 cs.CV、cs.LG、q-bio

AI总结 研究对基于全切片图像的基因表达预测的深度回归模型进行评估与验证，采用基于注意力的多实例学习与PFM特征提取器的直接回归，在多个数据集及队列中验证，证明该方法可泛化并恢复有意义分子结构，支持其用于转录组表型分析和风险分层。

MicroDiffuse3D: A Foundation Model for 3D Microscopy Imaging Restoration

MicroDiffuse3D：一种用于3D显微成像修复的预训练基础模型

Yongkang Li, Brian Wong, King Wai Chiu, Hanwen Xu, Tangqi Fang, Erin Dunnington, Dan Fu, Sheng Wang

机构 * Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA（保罗·G·艾伦计算机科学与工程学院，华盛顿大学，西雅图，华盛顿州，美国） ； Department of Chemistry, University of Washington, Seattle, WA, USA（化学系，华盛顿大学，西雅图，华盛顿州，美国） 纠错

专题命中 病理影像 ：diagnosis(abstract)；分类 cs.CV、cs.LG、q-bio

AI总结 本文提出MicroDiffuse3D，一种预训练的3D显微成像修复模型，通过高通量数据提升3D化学成像的分辨率和信噪比，实现高质量体体积结构重建。

Linking spatial biology and clinical histology via Haiku

通过俳句连接空间生物学与临床组织学

Yan Cui, Jacob S. Leiby, Wenhui Lei, Dokyoon Kim, Yanxiang Deng, Aaron T. Mayer, Zhenqin Wu, Alexandro E. Trevino, Zhi Huang

机构 * Department of Pathology and Laboratory Medicine, University of Pennsylvania（宾夕法尼亚大学病理学与实验室医学系） ； Department of Bioengineering, University of Pennsylvania（宾夕法尼亚大学生物工程系） ； Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania（宾夕法尼亚大学生物统计学、流行病学与信息学系） ； Enable Medicine, Menlo Park, CA, USA（Enable Medicine） 纠错

专题命中 病理影像 ：biomedical(abstract)；分类 cs.CV、cs.LG、q-bio

AI总结 本文提出Haiku模型，整合分子、形态和临床数据，通过三模态对比学习实现跨模态检索，提升分类和预测任务性能，并支持零样本生物标志物推断。

OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA

OpenTME：一个基于AI的H&E肿瘤微环境谱开放数据集

Maaike Galama, Nina Kozar-Gillan, Christina Embacher, Todd Dembo, Cornelius Böhm, Evelyn Ramberger, Julika Ribbat-Idel, Rosemarie Krupar, Verena Aumiller, Miriam Hägele, Kai Standvoss, Gerrit Erdmann, Blanca Pablos, Ari Angelo, Simon Schallenberg, Andrew Norgan, Viktor Matyas, Klaus-Robert Müller, Maximilian Alber, Lukas Ruff, Frederick Klauschen

机构 * Aignostics, Germany（德国Aignostics公司） ； Institute of Pathology, Charité – Universitätsmedizin Berlin, Germany（德国柏林Charité大学医学院病理研究所） ； Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, US（美国明尼苏达州罗切斯特梅奥诊所实验室医学与病理学部） ； Machine Learning Group, Technische Universität Berlin, Germany（德国柏林技术大学机器学习小组） ； BIFOLD – Berlin Institute for the Foundations of Learning and Data, Germany（德国柏林学习与数据基础研究所（BIFOLD）） ； Department of Artificial Intelligence, Korea University, Republic of Korea（韩国韩国大学人工智能系） ； Max-Planck Institute for Informatics, Germany（德国马克斯·普朗克信息研究所） ； German Cancer Research Center (DKFZ) & German Cancer Consortium (DKTK), Berlin & Munich Partner Sites, Germany（德国癌症研究中心（DKFZ）与德国癌症联盟（DKTK）柏林及慕尼黑合作伙伴站点） ； Institute of Pathology, Ludwig-Maximilians-Universität München, Germany（德国慕尼黑路德维希-马克西米利安大学病理研究所） ； Bavarian Cancer Research Center (BZKF), Germany（德国巴伐利亚癌症研究中心（BZKF）） 纠错

专题命中 病理影像 ：pathology(abstract)；分类 cs.CV、cs.LG、q-bio

AI总结 OpenTME通过AI技术对TCGA中的5种癌症类型H&E染色图像进行预计算微环境分析，提供超过4500个细胞级定量读数，支持空间生物学研究和计算方法开发。

Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype

可解释的基于组织形态学的胶质母细胞瘤、IDH野生型生存预测

Jan-Philipp Redlich, Friedrich Feuerhake, Stefan Nikolin, Nadine Sarah Schaadt, Sarah Teuber-Hanselmann, Joachim Weis, Sabine Luttmann, Andrea Eberle, Christoph Buck, Timm Intemann, Pascal Birnstill, Klaus Kraywinkel, Jonas Ort, Peter Boor, André Homeyer

机构 * Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所） ； Hannover Medical School（汉诺威医学院） ； Institute of Neuropathology, RWTH Aachen University Hospital（亚琛工业大学医院神经病理学研究所） ； Department of Neuropathology, Center for Pathology, Klinikum Bremen-Mitte（不来梅米特医院病理中心神经病理科） ； Bremen Cancer Registry, Leibniz Institute for Prevention Research and Epidemiology - BIPS（不来梅癌症登记处，莱布尼茨预防研究与流行病学研究所 - BIPS） ； Leibniz Institute for Prevention Research and Epidemiology - BIPS（莱布尼茨预防研究与流行病学研究所 - BIPS） ； Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光学、系统技术与图像 exploitation 研究所 IOSB） ； Robert Koch Institute（罗伯特·科赫研究所） ； Department of Neurosurgery, RWTH Aachen University Hospital（亚琛工业大学医院神经外科） ； Institute of Pathology, RWTH Aachen University Hospital（亚琛工业大学医院病理学研究所） ； Institute of Neuropathology, Medical Center - University of Freiburg（弗莱堡大学医学中心神经病理学研究所） ； Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD)（亚琛-波恩-科隆-杜塞尔多夫综合肿瘤中心 (CIO ABCD)） 纠错

专题命中 病理影像 ：diagnosis(abstract)；分类 cs.LG、q-bio、eess.IV

AI总结 本文提出一个可解释的AI框架，通过结合可解释的多实例学习和稀疏自编码器，利用组织形态学特征预测胶质母细胞瘤IDH野生型患者的生存情况，发现了一些显著的生存差异。

Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders

利用混合专家积变分自编码器改进多模态神经影像数据的规范建模

Sayantan Kumar, Philip Payne, Aristeidis Sotiras

机构 * Department of Computer Science and Engineering, Washington University in St. Louis, USA（计算机科学与工程系，华盛顿大学圣路易斯分校） ； Institute for Informatics, Data Science and Biostatistics, Washington University in St.Louis, USA（信息学、数据科学与生物统计研究所，华盛顿大学圣路易斯分校） ； Department of Radiology, Washington University in St.Louis, USA（放射学系，华盛顿大学圣路易斯分校） 纠错

专题命中 病理影像 ：pathology(abstract)；biomedical(comments,journal_ref)；分类 cs.LG

AI总结 本文提出利用混合专家积变分自编码器改进多模态神经影像数据的规范建模，以更准确地识别异常个体及异常脑区。

Comments IEEE Internattional Symposium in Biomedical Imaging 2024

Journal ref 2024 IEEE International Symposium on Biomedical Imaging (ISBI)

Benchmarking Foundation Models for Mitotic Figure Classification

对有监督模型进行基准测试以进行分裂图分类

Jonas Ammeling, Jonathan Ganz, Emely Rosbach, Ludwig Lausser, Christof A. Bertram, Katharina Breininger, Marc Aubreville

专题命中 病理影像 ：pathology(abstract)；biomedical(comments,journal_ref)；分类 cs.CV

AI总结 本研究通过LoRA调整基础模型，在仅用10%训练数据的情况下达到接近100%性能，并在未见领域中表现优异。

Comments Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2026:003

Journal ref Machine.Learning.for.Biomedical.Imaging. 2026 (2026)