医学 AI

Ensuring accurate stain reproduction in deep generative networks for virtual immunohistochemistry

Christopher D. Walsh, Joanne Edwards, Robert H. Insall

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

Comments Eighteen pages, six figures

A Standardized Pipeline for Colon Nuclei Identification and Counting Challenge

Jijun Cheng, Xipeng Pan, Feihu Hou, Bingchao Zhao, Jiatai Lin, Zhenbing Liu, Zaiyi Liu, Chu Han

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

Weakly-supervised learning for image-based classification of primary melanomas into genomic immune subgroups

Lucy Godson, Navid Alemi, Jeremie Nsengimana, Graham P. Cook, Emily L. Clarke, Darren Treanor, D. Timothy Bishop, Julia Newton-Bishop, Ali Gooya

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

Comments 8 pages (without appendices), 2 figures, submitted to MIDL 2022 conference proceedings

Novel Local Radiomic Bayesian Classifiers for Non-Invasive Prediction of MGMT Methylation Status in Glioblastoma

Mihir Rao

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

A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning

Lantian Zhang, Mohamed Amgad, Lee A. D. Cooper

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

Comments 7 pages, 4 figures, 4 tables

Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels

Álvaro García Faura, Dejan Štepec, Tomaž Martinčič, Danijel Skočaj

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

Comments Accepted to SPIE Medical Imaging conference

Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels

Chu Han, Jiatai Lin, Jinhai Mai, Yi Wang, Qingling Zhang, Bingchao Zhao, Xin Chen, Xipeng Pan, Zhenwei Shi, Xiaowei Xu, Su Yao, Lixu Yan, Huan Lin, Zeyan Xu, Xiaomei Huang, Guoqiang Han, Changhong Liang, Zaiyi Liu

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

Comments 15 pages, 10 figures, journal

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

Trevor Doherty, Susan McKeever, Nebras Al-Attar, Tiarnan Murphy, Claudia Aura, Arman Rahman, Amanda O'Neill, Stephen P Finn, Elaine Kay, William M. Gallagher, R. William G. Watson, Aoife Gowen, Patrick Jackman

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

Comments 19 pages, 8 tables, 18 figures

MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

Zakaria Senousy, Mohammed M. Abdelsamea, Mohamed Medhat Gaber, Moloud Abdar, U Rajendra Acharya, Abbas Khosravi, Saeid Nahavandi

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

Comments accepted by IEEE Transactions on Biomedical Engineering

Journal ref IEEE Transactions on Biomedical Engineering 2021

Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks

Richard J. Chen, Ming Y. Lu, Muhammad Shaban, Chengkuan Chen, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood

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

Comments MICCAI 2021

TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer

Fan Wang, Saarthak Kapse, Steven Liu, Prateek Prasanna, Chao Chen

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

Comments 12 pages, 5 figures, 2 tables, accepted to International Conference on Information Processing in Medical Imaging (IPMI) 2021

Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment

David Joon Ho, Narasimhan P. Agaram, Peter J. Schueffler, Chad M. Vanderbilt, Marc-Henri Jean, Meera R. Hameed, Thomas J. Fuchs

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

Comments Accepted at MICCAI 2020

Uncovering the structure of clinical EEG signals with self-supervised learning

Hubert Banville, Omar Chehab, Aapo Hyvärinen, Denis-Alexander Engemann, Alexandre Gramfort

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

Comments 32 pages, 9 figures

Deep learning-based survival prediction for multiple cancer types using histopathology images

Ellery Wulczyn, David F. Steiner, Zhaoyang Xu, Apaar Sadhwani, Hongwu Wang, Isabelle Flament, Craig H. Mermel, Po-Hsuan Cameron Chen, Yun Liu, Martin C. Stumpe

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

Journal ref PLOS ONE (2020)

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

Miriam Hägele, Philipp Seegerer, Sebastian Lapuschkin, Michael Bockmayr, Wojciech Samek, Frederick Klauschen, Klaus-Robert Müller, Alexander Binder

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

Journal ref Sci Rep 10, 6423 (2020)

Joint 2D-3D Breast Cancer Classification

Gongbo Liang, Xiaoqin Wang, Yu Zhang, Xin Xing, Hunter Blanton, Tawfiq Salem, Nathan Jacobs

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

Comments Accepted by IEEE International Conference of Bioinformatics and Biomedicine (BIBM), 2019

Neural parameters estimation for brain tumor growth modeling

Ivan Ezhov, Jana Lipkova, Suprosanna Shit, Florian Kofler, Nore Collomb, Benjamin Lemasson, Emmanuel Barbier, Bjoern Menze

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

Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network

Yongxiang Huang, Albert Chi-shing Chung

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

Comments 8 pages, MICCAI workshop preceedings

Journal ref Computational Pathology and Ophthalmic Medical Image Analysis. Springer, Cham, 2018. 19-26

BACH: Grand Challenge on Breast Cancer Histology Images

Guilherme Aresta, Teresa Araújo, Scotty Kwok, Sai Saketh Chennamsetty, Mohammed Safwan, Varghese Alex, Bahram Marami, Marcel Prastawa, Monica Chan, Michael Donovan, Gerardo Fernandez, Jack Zeineh, Matthias Kohl, Christoph Walz, Florian Ludwig, Stefan Braunewell, Maximilian Baust, Quoc Dang Vu, Minh Nguyen Nhat To, Eal Kim, Jin Tae Kwak, Sameh Galal, Veronica Sanchez-Freire, Nadia Brancati, Maria Frucci, Daniel Riccio, Yaqi Wang, Lingling Sun, Kaiqiang Ma, Jiannan Fang, Ismael Kone, Lahsen Boulmane, Aurélio Campilho, Catarina Eloy, António Polónia, Paulo Aguiar

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

Comments Accepted for publication at Medical Image Analysis (Elsevier). Publication licensed under the Creative Commons CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Journal ref Medical Image Analysis, 2019

Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results

Vishwa S. Parekh, John Laterra, Chetan Bettegowda, Alex E. Bocchieri, Jay J. Pillai, Michael A. Jacobs

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

Comments 6 pages, 4 figure, 2 tables, radiomics, brain

Gravitational cell detection and tracking in fluorescence microscopy data

Nikomidisz Eftimiu, Michal Kozubek

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

Comments 5 pages, 2 figures, 1 formula, 1 table, submitted to the 21st International Symposium on Biomedical Imaging (ISBI 2024)

Preserving Dense Features for Ki67 Nuclei Detection

Seyed Hossein Mirjahanmardi, Melanie Dawe, Anthony Fyles, Wei Shi, Fei-Fei Liu, Susan Done, April Khademi

专题命中 病理影像 ：pathology(abstract,comments)；分类 q-bio、eess.IV

Comments Published in SPIE Medical Imaging 04/2022: Digital and Computational Pathology; 120390Y. Event: SPIE Medical Imaging, 2022, San Diego, California, United States

LUNG-KGMM: Knowledge-Guided Multimodal Learning for Lung Cancer Incidence Prediction

LUNG-KGMM：用于肺癌发病预测的知识引导多模态学习

Chunlei Yang, Shuyan Li, Zhong Cao

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

AI总结 本研究提出LUNG-KGMM知识引导多模态框架，整合多类数据与临床知识开展1-6年肺癌发病预测，经MIMIC及厦门队列验证，其性能优于现有方法且具备跨队列可迁移性。

Comments 22 pages, 4 figures, 7 tables, accepted by PRCV Oral

Journal ref The 9th Chinese Conference on Pattern Recognition and Computer Vision, PRCV2026

IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions

IMPLICITSTAINER：基于神经隐式函数的无分辨率依赖数据高效虚拟染色

Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian

机构 * Kahlert School of Computing, University of Utah（犹他大学卡勒特计算学院） ； Department of Pathology, University of Utah（犹他大学病理学系） 纠错

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

AI总结 本文提出IMPLICITSTAINER，通过神经隐式深度学习模型将H&E图像转化为IHC图像，实现无分辨率依赖的高效虚拟染色，提升低数据场景下的鲁棒性与输出确定性。

XtraLight-MedMamba for Classification of Neoplastic Tubular Adenomas

XtraLight-MedMamba用于神经内分泌瘤分类

Aqsa Sultana, Rayan Afsar, Ahmed Rahu, Surendra P. Singh, Brian Shula, Brandon Combs, Derrick Forchetti, Vijayan K. Asari

机构 * Vision Lab and Dept. of Electrical and Computer Engineering, University of Dayton（视觉实验室和电气与计算机工程系，代顿大学） ； Dept. of Computer Science, University of Georgia（计算机科学系，佐治亚大学） ； The University of Toledo Medical Center（托莱多大学医学中心） ； Honeywell International Inc.（霍尼韦尔国际公司） ； South Bend Medical Foundation（南本迪医疗基金会） 纠错

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

AI总结 本文提出XtraLight-MedMamba模型，通过轻量级状态空间框架对全滑片图像中的神经内分泌瘤进行分类，利用ConvNeXt和Mamba块结合SCAB模块和FNOClassifier，实现高精度分类。

Comments 18 pages, 11 figures

Gauge-Fixing the Forward-Forward Objective: A Whitened Goodness Derived from a Likelihood-Ratio Account

善度衡量什么？前馈学习的似然比解释

Paolo Giannitrapani

机构 * Sapienza University of Rome（罗马第一大学） 纠错

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

AI总结 研究前馈学习中善度衡量问题，基于显式生成模型指出平方善度是似然比检验充分统计量，FF阈值是其边界，还阐述了该方法在不同总体及层间归一化方面的特性及优势。

Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide images

通过H&E染色的整张滑片图像评估早期乳腺癌复发风险

Geongyu Lee, Joonho Lee, Tae-Yeong Kwak, Sun Woo Kim, Youngmee Kwon, Chungyeul Kim, Hyeyoon Chang

机构 * Research Team（研究团队） ； Deep Bio Inc.（Deep Bio公司） ； National Cancer Center（国立癌症中心） ； Korea University Guro Hospital（韩国大学Guro医院） 纠错

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

AI总结 本研究利用深度学习分析H&E染色的整张滑片图像，以预测早期乳腺癌复发风险，展示了该方法在风险分层中的潜力。

Comments 20 pages, 9 figures

Journal ref Scientific Reports 15, 35069 (2025)

Gradient-Based Latent Decomposition Reveals Mechanisms of Feature Degradation in Weakly Supervised Mammography

基于梯度的潜在分解揭示了弱监督乳腺钼靶成像中特征退化的机制

Vinceline Bertrand, Ionut Cardei

机构 * Florida Atlantic University（佛罗里达大西洋大学） 纠错

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

AI总结 研究弱监督乳腺钼靶成像中特征退化问题，引入基于梯度的正交潜在分解用于分层变分自编码器，划分潜在空间，通过实验得出模型相关指标，经潜在消融等验证，揭示高维空间中粗监督信号对细粒度特征的影响。

Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions

用于长尾分布的频谱感知分析类增量学习

Quyen Tran, Hai Nguyen, Quan Dao, Zhuowei Li, Nam Le, Trung Le, Dimitris Metaxas

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

AI总结 研究针对长尾分布的类增量学习问题，提出几何频谱校正（GSR）框架，将其视为频谱正则化问题，构造频谱扰动矩阵有选择地增大尾部类坍缩特征值，实验表明该方法在计算效率和鲁棒泛化间取得更好权衡，达新最优水平。

Comments ECCV 2026

Atlas 2 -- Foundation models for clinical deployment

Atlas 2 -- 临床应用的基础模型

Maximilian Alber, Timo Milbich, Alexandra Carpen-Amarie, Stephan Tietz, Jonas Dippel, Lukas Muttenthaler, Beatriz Perez Cancer, Alessandro Benetti, Panos Korfiatis, Elias Eulig, Jérôme Lüscher, Jiasen Wu, Sayed Abid Hashimi, Gabriel Dernbach, Simon Schallenberg, Neelay Shah, Moritz Krügener, Aniruddh Jammoria, Jake Matras, Patrick Duffy, Matt Redlon, Philipp Jurmeister, David Horst, Lukas Ruff, Klaus-Robert Müller, Frederick Klauschen, Andrew Norgan

机构 * Aignostics, Germany（德国Aignostics公司） ； Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, US（美国梅奥诊所实验室医学与病理学部门） ； Department of Radiology, Mayo Clinic, Rochester MN, US（美国梅奥诊所放射学部门） ； Department of Information Technology, Mayo Clinic, Rochester MN, US（美国梅奥诊所信息技术部门） ； Mayo Clinic, Rochester MN, US（美国梅奥诊所） ； Digital Pathology, Mayo Clinic, Rochester MN, US（美国梅奥诊所数字病理学部门） ； 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） ； 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）及德国癌症联盟（DKTK）柏林与慕尼黑合作站点） ； 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学研究所） ； Institute of Pathology, Charité – Universitätsmedizin Berlin, Germany（德国柏林夏里特大学医学中心病理学研究所） ； Bavarian Cancer Research Center (BZKF), Germany（德国巴伐利亚癌症研究中心（BZKF）） ； Helmholtz Munich, Germany（德国海德堡-慕尼黑亥姆霍兹中心） ； Technical University Munich, Germany（德国慕尼黑技术大学） 纠错

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

AI总结 Atlas 2系列通过在80个公开基准测试中展现卓越的预测性能、鲁棒性和资源效率，为临床应用提供了先进的病理视觉基础模型。