医学 AI

ConvPath: A Software Tool for Lung Adenocarcinoma Digital Pathological Image Analysis Aided by Convolutional Neural Network

Shidan Wang, Tao Wang, Lin Yang, Faliu Yi, Xin Luo, Yikun Yang, Adi Gazdar, Junya Fujimoto, Ignacio I. Wistuba, Bo Yao, ShinYi Lin, Yang Xie, Yousheng Mao, Guanghua Xiao

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

Convolutional capsule network for classification of breast cancer histology images

Tomas Iesmantas, Robertas Alzbutas

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

Comments Submitted to ICIAR 2018

Deep learning enhanced mobile-phone microscopy

Yair Rivenson, Hatice Ceylan Koydemir, Hongda Wang, Zhensong Wei, Zhengshuang Ren, Harun Gunaydin, Yibo Zhang, Zoltan Gorocs, Kyle Liang, Derek Tseng, Aydogan Ozcan

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

Journal ref ACS Photonics (2018)

RetiNet: Automatic AMD identification in OCT volumetric data

Stefanos Apostolopoulos, Carlos Ciller, Sandro I. De Zanet, Sebastian Wolf, Raphael Sznitman

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

Comments 14 pages, 10 figures, Code available

Hierarchical Self-Supervised Adversarial Training for Robust Vision Models in Histopathology

层次化自监督对抗训练用于组织病理学中的鲁棒视觉模型

Hashmat Shadab Malik, Shahina Kunhimon, Muzammal Naseer, Fahad Shahbaz Khan, Salman Khan

机构 * Mohamed Bin Zayed University of Artificial Intelligence（Mohamed Bin Zayed人工智能大学） ； Khalifa University（卡勒比大学） ； Linköping University（林霍尔姆大学） ； Australian National University（澳大利亚国立大学） 纠错

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

AI总结 提出层次化自监督对抗训练（HSAT），利用组织病理图像的患者-切片-补丁层次结构进行多级对比学习，生成对抗样本并整合到对抗训练中，在OpenSRH数据集上白盒设置平均提升54.31%，黑盒设置性能下降降至3-4%。

Comments Accepted at 28th International Conference On Medical Image Computing And Computer Assisted Intervention (MICCAI 2025)

Enhancing Histopathological Image Classification via Integrated HOG and Deep Features with Robust Noise Performance

通过集成HOG和深度特征提升组织病理图像分类性能

Ifeanyi Ezuma, Ugochukwu Ugwu

机构 * InceptionResNet-v2 network（InceptionResNet-v2网络） 纠错

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

AI总结 本文研究了在LC25000数据集上使用HOG和深度特征提升图像分类性能，发现深度特征提取模型在噪声环境下具有更强鲁棒性。

Comments 10 pages, 8 figures. Code and datasets available upon request

Journal ref Proc. SPIE 13932, Medical Imaging 2026: Digital and Computational Pathology, 1393216 (2026)

Prompt-Free Lightweight SAM Adaptation for Histopathology Nuclei Segmentation with Strong Cross-Dataset Generalization

无需提示的轻量级SAM适应用于病理学核分割的强跨数据集泛化

Muhammad Hassan Maqsood, Yanming Zhu, Alfred Lam, Getamesay Dagnaw, Xuefei Yin, Alan Wee-Chung Liew

机构 * School of Information and Communication Technology, Griffith University, QLD, Australia（信息与通信技术学院，格里菲斯大学，昆士兰，澳大利亚） ； School of MDP -- Clinical Medicine, Griffith University, QLD, Australia（MDP临床医学学院，格里菲斯大学，昆士兰，澳大利亚） 纠错

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

AI总结 本文提出一种无需提示的轻量级SAM适应方法，通过多级编码器特征和残差解码实现高效准确的核分割，实验表明其在三个基准数据集上表现优异，具有强跨数据集泛化能力。

Journal ref IEEE International Symposium on Biomedical Imaging (Oral) 2026

MMSF: Multitask and Multimodal Supervised Framework for WSI Classification and Survival Analysis

MMSF：多任务和多模态监督框架用于WSI分类和生存分析

Chengying She, Chengwei Chen, Xinran Zhang, Ben Wang, Lizhuang Liu, Chengwei Shao, Yun Bian

机构 * University of Chinese Academy of Sciences（中国科学院大学） ； Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海先进研究院） ； Department of Radiology, Changhai Hospital（昌海医院放射科） 纠错

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

AI总结 MMSF通过多任务和多模态监督框架，结合组织拓扑和临床数据，提升全滑片图像分类和生存分析的准确性和预后预测能力。

Comments Submitted to "Biomedical Signal Processing and Control"

Beyond Occlusion: In Search for Near Real-Time Explainability of CNN-Based Prostate Cancer Classification

超越遮挡：在寻找基于CNN的前列腺癌分类的近实时可解释性

Martin Krebs, Jan Obdržálek, Vít Musil, Tomáš Brázdil

机构 * Faculty of Informatics, Masaryk University, Brno, Czech Republic（信息学院，马萨里克大学，布拉格，捷克共和国） 纠错

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

AI总结 本文提出了一种更快的遮挡替代方法，用于基于CNN的前列腺癌分类，显著提高了解释效率，促进临床应用的实现。

Journal ref 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)

Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy

探讨术前MRI描述符在预测乳腺癌新辅助化疗病理完全缓解中的附加价值

Caroline Malhaire, Fatine Selhane, Marie-Judith Saint-Martin, Vincent Cockenpot, Pia Akl, Enora Laas, Audrey Bellesoeur, Catherine Ala Eddine, Melodie Bereby-Kahane, Julie Manceau, Delphine Sebbag-Sfez, Jean-Yves Pierga, Fabien Reyal, Anne Vincent-Salomon, Herve Brisse, Frederique Frouin

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

AI总结 本研究探讨术前MRI特征在预测乳腺癌新辅助化疗病理完全缓解中的作用，发现非分叶边缘和单发性是独立预测因素，可提高预测模型性能。

Journal ref European Radiology, 2023, 33 (11), pp.8142-8154

MiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Classification and Detection

Andrea Moglia, Elia Clement Nastasio, Luca Mainardi, Pietro Cerveri

机构 * Department of Electronics, Information, and Bioengineering（电子、信息与生物工程系） ； Polytechnic University of Milan（米兰理工学院） ； Department of Industrial, and Information Engineering（工业与信息工程系） ； University of Pavia（帕维亚大学） 纠错

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

Journal ref Moglia, A., Nastasio, E.C., Mainardi, L. et al. MiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Observation and Localization in CT Images. J Healthc Inform Res (2025)

Adaptive Stain Normalization for Cross-Domain Medical Histology

Tianyue Xu, Yanlin Wu, Abhai K. Tripathi, Matthew M. Ippolito, Benjamin D. Haeffele

机构 * Dept. of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA（生物医学工程系，约翰霍普金斯大学，巴尔的摩，MD，USA） ； Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Baltimore, MD, USA（约翰霍普金斯疟疾研究 institute，W. Harry Feinstone 分子微生物学与免疫学系，布隆伯格公共卫生学院，巴尔的摩，MD，USA） ； Dept. of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA（医学系，约翰霍普金斯大学医学院，巴尔的摩，MD，USA） ； Dept. of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA（电气与系统工程系，宾夕法尼亚大学，费城，PA，USA） 纠错

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

Comments Accepted to the 28th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2025)

Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort

Iulian Emil Tampu, Per Nyman, Christoforos Spyretos, Ida Blystad, Alia Shamikh, Gabriela Prochazka, Teresita Díaz de Ståhl, Johanna Sandgren, Peter Lundberg, Neda Haj-Hosseini

机构 * Department of Biomedical Engineering, Linköping University, Sweden（生物医学工程系，利厄普大学） ； Center for Medical Image Science and Visualization, Linköping University, Sweden（医学影像科学与可视化中心，利厄普大学） ； Crown Princess Victoria Children’s Hospital and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（维多利亚公主儿童医院和健康、医学与关怀科学系，利厄普大学） ； Department of Radiology and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（放射科和健康、医学与关怀科学系，利厄普大学） ； Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden（肿瘤病理学系，卡罗林斯卡研究所，索纳） ； Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Sweden（临床病理学和癌症诊断系，卡罗林斯卡大学医院） ； Department of Radiation Physics and Department of Medical and Health Sciences, Linköping University, Sweden（放射物理系和医学与健康科学系，利厄普大学） 纠错

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

Journal ref Tampu IE et al. Pediatric brain tumor classification using digital pathology and deep learning: Evaluation of SOTA methods on a multi-center Swedish cohort. Brain Pathology. 2025. e70029

Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtyping

Pablo Meseguer, Rocío del Amor, Valery Naranjo

机构 * Universitat Politècnica de València (UPV)（瓦伦西亚理工大学） ； Artikode Intelligence S.L.（Artikode Intelligence公司） 纠错

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

Comments Accepeted for oral presentation at Medical Image Understanding and Analysis (MIUA) 2025

OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport

Qin Ren, Yifan Wang, Ruogu Fang, Haibin Ling, Chenyu You

机构 * Department of Computer Science, Stony Brook University（计算机科学系，石英 Brook 大学） ； Department of Applied Mathematics & Statistics, Stony Brook University（应用数学与统计系，石英 Brook 大学） ； Department of Biomedical Engineering, University of Florida（生物医学工程系，佛罗里达大学） 纠错

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

Comments Accepted by International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2025)

Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancement

Biwen Meng, Xi Long, Wanrong Yang, Ruochen Liu, Yi Tian, Yalin Zheng, Jingxin Liu

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

Comments 2025 IEEE International Symposium on Biomedical Imaging (ISBI)

Rethinking Pre-Trained Feature Extractor Selection in Multiple Instance Learning for Whole Slide Image Classification

Bryan Wong, Sungrae Hong, Mun Yong Yi

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

Comments Accepted to IEEE International Symposium on Biomedical Imaging (ISBI) 2025

Low-resource finetuning of foundation models beats state-of-the-art in histopathology

Benedikt Roth, Valentin Koch, Sophia J. Wagner, Julia A. Schnabel, Carsten Marr, Tingying Peng

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

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

Automated Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy from DWI Data

Shir Nitzan, Maya Gilad, Moti Freiman

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

Comments Accepted for presentation at the IEEE International Symposium on Biomedical Imaging (ISBI)

Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models

Raza Imam, Mohammed Talha Alam

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

Comments Our code is available at https://github.com/Razaimam45/AI701-Project-Transfer-Learning-approach-for-imbalance-classification-of-Brain-Tumor-MRI-

Journal ref E-pi UAI workshop (UAI 2023)

An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Location Deep Features

Ziquan Wei, Shenghua Cheng, Junbo Hu, Li Chen, Shaoqun Zeng, Xiuli Liu

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

Comments 15 pages, 8 figures, under review to Medical Image Analysis (2nd round)

RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification

Shujun Wang, Yaxi Zhu, Lequan Yu, Hao Chen, Huangjing Lin, Xiangbo Wan, Xinjuan Fan, Pheng-Ann Hen

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

Comments Accepted at Medical Image Analysis. Code: https://github.com/EmmaW8/RMDL

Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection

Timo Kohlberger, Yun Liu, Melissa Moran, Po-Hsuan, Chen, Trissia Brown, Craig H. Mermel, Jason D. Hipp, Martin C. Stumpe

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

Journal ref Pathology Informatics (2019)

Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases

Amit Kumar Jaiswal, Ivan Panshin, Dimitrij Shulkin, Nagender Aneja, Samuel Abramov

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

Comments Accepted in CVPR 2019 Workshop Towards Causal, Explainable and Universal Medical Visual Diagnosis

Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution

Francis Tom, Himanshu Sharma, Dheeraj Mundhra, Tathagato Rai Dastidar, Debdoot Sheet

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

Comments To appear in the Proceedings of the 2019 IEEE International Symposium on Biomedical Imaging (ISBI 2019)

Non-invasive imaging of Young's modulus and Poisson's ratio in cancers in vivo

Md Tauhidul Islam, Songyuan Tang, Chiara Liverani, Ennio Tasciotti, Raffaella Righetti

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

Comments 38 pages, 34 figures, Submitted to IEEE Transactions on Biomedical Engineering

Domain-adversarial neural networks to address the appearance variability of histopathology images

Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta

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

Comments MICCAI 2017 Workshop on Deep Learning in Medical Image Analysis

A deep learning model integrating FCNNs and CRFs for brain tumor segmentation

Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yazhuo Zhang, Yong Fan

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

Comments This version was accepted in the journal Medical Image Analysis

Context-based Normalization of Histological Stains using Deep Convolutional Features

Daniel Bug, Steffen Schneider, Anne Grote, Eva Oswald, Friedrich Feuerhake, Julia Schüler, Dorit Merhof

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

Comments In: 3rd Workshop on Deep Learning in Medical Image Analysis (DLMIA 2017)

Transitioning between Convolutional and Fully Connected Layers in Neural Networks

Shazia Akbar, Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne Martel

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

Comments This work is to appear at the 3rd workshop on Deep Learning in Medical Image Analysis (DLMIA), MICCAI 2017