医学 AI

Brain Tumor Synthetic Data Generation with Adaptive StyleGANs

Usama Tariq, Rizwan Qureshi, Anas Zafar, Danyal Aftab, Jia Wu, Tanvir Alam, Zubair Shah, Hazrat Ali

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

Comments Accepted in AICS conference

Representation Learning for Non-Melanoma Skin Cancer using a Latent Autoencoder

Simon Myles Thomas

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

Comments 5 figures, 11 pages

Continual Learning for Tumor Classification in Histopathology Images

Veena Kaustaban, Qinle Ba, Ipshita Bhattacharya, Nahil Sobh, Satarupa Mukherjee, Jim Martin, Mohammad Saleh Miri, Christoph Guetter, Amal Chaturvedi

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

Comments Accepted by MOVI, a MICCAI2022 workshop: https://sites.google.com/view/movi2022

OncoPetNet: A Deep Learning based AI system for mitotic figure counting on H&E stained whole slide digital images in a large veterinary diagnostic lab setting

Michael Fitzke, Derick Whitley, Wilson Yau, Fernando Rodrigues, Vladimir Fadeev, Cindy Bacmeister, Chris Carter, Jeffrey Edwards, Matthew P. Lungren, Mark Parkinson

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

Colorectal Cancer Segmentation using Atrous Convolution and Residual Enhanced UNet

Nisarg A. Shah, Divij Gupta, Romil Lodaya, Ujjwal Baid, Sanjay Talbar

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

Comments 5th IAPR International Conference on Computer Vision and Image Processing, 12 pages

Blind stain separation using model-aware generative learning and its applications on fluorescence microscopy images

Xingyu Li

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

Comments Accepted by IPML2021

Performance Comparison of Balanced and Unbalanced Cancer Datasets using Pre-Trained Convolutional Neural Network

Ali Narin

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

Comments Presented for International Conference on Artificial Intelligence towards Industry 4.0 (ICAII4.0 2020)

Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs

Kushal Mehta, Arshita Jain, Jayalakshmi Mangalagiri, Sumeet Menon, Phuong Nguyen, David R. Chapman

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

Comments This paper has been submitted to the Journal of Digital Imaging (JDI 2020). The poster of this paper has received the 2nd prize for the Research Poster Award. Link: https://siim.org/page/20m_p_lung_node_malignancy

Learning a low dimensional manifold of real cancer tissue with PathologyGAN

Adalberto Claudio Quiros, Roderick Murray-Smith, Ke Yuan

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

Automatic MRI-Driven Model Calibration for Advanced Brain Tumor Progression Analysis

Klaudius Scheufele, Shashank Subramanian, George Biros

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

Comments 10 pages, 5 figures

A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imaging

Kianoush Falahkheirkhah, Kevin Yeh, Shachi Mittal, Luke Pfister, Rohit Bhargava

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

Comments corrected typos (the word "lack" was missing in the abstract)

Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence

Shivam Kalra, H. R. Tizhoosh, Sultaan Shah, Charles Choi, Savvas Damaskinos, Amir Safarpoor, Sobhan Shafiei, Morteza Babaie, Phedias Diamandis, Clinton JV Campbell, Liron Pantanowitz

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

CHIMERA Challenge: Biochemical Recurrence Prediction in Prostate Cancer Patients using multimodal datasets

CHIMERA挑战赛：利用多模态数据集预测前列腺癌患者的生化复发

Robert N. Spaans, Catherine Chia, Tongjie Wang, Adam Kowalewski, Parandzem Khachatryan, Domingos Oliveira, Khrystyna Faryna, Jean-Paul A. van Basten, Geert Litjens, Nadieh Khalili

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

AI总结 本研究开发首个公开标准化前列腺癌预后多模态基准CHIMERA挑战赛，整合多类数据建立数据集，发现多模态模型在缺失临床变量时更稳健，单模态临床模型性能更高但对变量完整性敏感。

Comments 38 pages, 3 figures, 3 supplementary figures. Preprint submitted to Medical Image Analysis. Challenge results presented at the CHIMERA workshop, MICCAI 2025. Challenge website: https://chimera.grand-challenge.org/chimera/

Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization

Mara Graziani, Sebastian Otalora, Stephane Marchand-Maillet, Henning Muller, Vincent Andrearczyk

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

Comments Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:011

End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction

Anindo Saha, Matin Hosseinzadeh, Henkjan Huisman

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

Comments Accepted to MedIA: Medical Image Analysis. This manuscript incorporates and expands upon our 2020 Medical Imaging Meets NeurIPS Workshop paper (arXiv:2011.00263)

An attention-based multi-resolution model for prostate whole slide imageclassification and localization

Jiayun Li, Wenyuan Li, Arkadiusz Gertych, Beatrice S. Knudsen, William Speier, Corey W. Arnold

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

Comments 8 pages, 4 figures, CVPR 2019 Towards Causal, Explainable and Universal Medical Visual Diagnosis (MVD) Workshop

Exploring learning environments for label\-efficient cancer diagnosis

探索用于标签高效癌症诊断的学习环境

Samta Rani, Tanvir Ahmad, Sarfaraz Masood, Chandni Saxena

专题命中 病理影像 ：diagnosis(title)；分类 cs.CV

AI总结 本研究考察监督学习、半监督学习、自监督学习三种学习环境，用三种预训练模型在七个训练集上预测肾癌、肺癌、乳腺癌，发现半监督学习结果与监督学习高度一致，是标签受限场景的高可行替代方案。

Comments The submission is being withdrawn due to an authorship disagreement among the co-authors

A Multi-Cohort Validation of Censoring-Aware Conformal Lower Predictive Bounds for Pathology Survival Models

面向病理生存模型的感知删失共形下界预测边界的多队列验证

Mingi Hong

专题命中 病理影像 ：pathology(title)；分类 cs.LG

AI总结 本研究通过多队列验证，评估了用于病理生存模型的感知删失共形下界预测边界方法drcosarc，发现其性能依赖于队列，解释受患者单元、估计量和删失假设影响。

Comments 22 pages, 3 figures, 2 main tables, and supplementary material. Preprint

PathSelect: Sequential Token Selection for Whole Slide Pathology

PathSelect：用于全切片病理学的序列令牌选择

Jingzhi Chen, Landi He, Zehong Chen, Peihang Wu, Lijian Xu

机构 * Shenzhen University of Advanced Technology（深圳先进技术研究院） 纠错

专题命中 病理影像 ：pathology(title)；分类 cs.CV

AI总结 针对全切片图像给视觉语言模型带来的计算瓶颈，提出解耦路由框架PathSelect，将令牌剪枝重构成序列选择过程，训练时引入方差保持噪声门等，推理时模块分离，有效减少令牌数，提高准确率并优于基于采样的方法。

From Patches to Patients: A study of the tile-to-slide performance transferability in Digital Pathology

从斑块到患者：数字病理学中斑块到全切片性能可迁移性的研究

Sofiène Boutaj, Leo Fillioux, Maria Vakalopoulou, Stergios Christodoulidis, Pierre Marza

机构 * Université Paris-Saclay, CentraleSupélec, Gustave Roussy, INSERM, IHU PRISM, Cancer Data Science Unit（巴黎-萨克雷大学、中央理工-高等电力学院、古斯塔夫·鲁西研究所、法国国家健康与医学研究院、IHU PRISM、癌症数据科学单元） ； Université Paris-Saclay, CentraleSupélec, MICS Laboratory（巴黎-萨克雷大学、中央理工-高等电力学院、MICS实验室） 纠错

专题命中 病理影像 ：pathology(title)；分类 cs.CV

AI总结 研究斑块级线性探测能否作为全切片级性能的可靠代理，通过19个基础模型在42个切片级和16个斑块级任务上的基准测试，发现斑块与切片性能高度相关，斑块级基准测试可有效筛选候选模型。

Comments Accepted to MICCAI 2026

Aligning Cellular Sheaves with Classifier Attention for Interpretable Weakly-Supervised Pathology Localization

对齐细胞层与分类器注意力以实现可解释的弱监督病理定位

Devansh Lalwani, Swapnil Bhat, Maulik Shah

机构 * Turocrates AI Private Limited（Turocrates AI私有有限公司） 纠错

专题命中 病理影像 ：pathology(title)；分类 cs.CV

AI总结 针对弱监督全切片图像分类中注意力图定位不准确的问题，提出结合细胞层与注意力机制的一致性训练方法，在Camelyon16上实现补丁级AUC 0.940，并提升注意力AUC从0.717至0.953。

VISTA: Validation-Guided Integration of Spatial and Temporal Foundation Models with Anatomical Decoding for Rare-Pathology VCE Event Detection -- after competition results

VISTA：基于解剖解码的时空基础模型验证引导集成用于罕见病理VCE事件检测——竞赛结果后

Bo-Cheng Qiu, Fang-Ying Lin, Ming-Han Sun, Yu-Fan Lin, Chia-Ming Lee, Chih-Chung Hsu

机构 * National Cheng Kung University, Taiwan（国立成功大学, 台湾） ； National Yang Ming Chiao Tung University, Taiwan（国立阳明交通大学, 台湾） 纠错

专题命中 病理影像 ：pathology(title)；分类 cs.CV

AI总结 本文提出VISTA框架，结合时空基础模型和解剖解码，通过验证引导的加权融合和时间事件解码，提升罕见病理VCE事件检测的性能，竞赛后进一步优化后取得第二名。

VISTA: Validation-Guided Integration of Spatial and Temporal Foundation Models with Anatomical Decoding for Rare-Pathology VCE Event Detection

VISTA：基于解剖解码的时空基础模型验证引导整合用于罕见病理VCE事件检测

Bo-Cheng Qiu, Yu-Fan Lin, Yu-Zhe Pien, Chia-Ming Lee, Fu-En Yang, Yu-Chiang Frank Wang, Chih-Chung Hsu

机构 * National Cheng Kung University（国立成功大学） ； National Yang Ming Chiao Tung University（国立阳明交通大学） ； NVIDIA 纠错

专题命中 病理影像 ：pathology(title)；分类 cs.CV

AI总结 本文提出VISTA方法，通过整合时空基础模型与解剖解码，提升罕见病理事件检测的准确性，实验显示其在隐藏测试集上达到较高的mAP指标。

Advancing Cancer Prognosis with Hierarchical Fusion of Genomic, Proteomic and Pathology Imaging Data from a Systems Biology Perspective

从系统生物学视角出发，通过多层次融合基因组、蛋白质组和病理影像数据以推进癌症预后

Junjie Zhou, Bao Xue, Meiling Wang, Wei Shao, Daoqiang Zhang

专题命中 病理影像 ：pathology(title)；分类 cs.CV

AI总结 本文提出HFGPI框架，通过分子编码策略和蛋白质引导超图学习，多层次融合基因组、蛋白质组和病理影像数据，提升癌症预后预测精度。

LungCRCT: Causal Representation based Lung CT Processing for Lung Cancer Treatment

LungCRCT: 基于因果表示的肺癌处理用于肺癌治疗

Daeyoung Kim

专题命中 病理影像 ：CT(title)；分类 cs.CV

AI总结 LungCRCT通过基于因果表示的学习框架，提升肺癌治疗中的因果干预分析和肿瘤分类性能。

PixCell: A generative foundation model for digital histopathology images

PixCell：数字病理图像的生成基础模型

Srikar Yellapragada, Alexandros Graikos, Zilinghan Li, Kostas Triaridis, Varun Belagali, Tarak Nath Nandi, Karen Bai, Beatrice S. Knudsen, Tahsin Kurc, Rajarsi R. Gupta, Prateek Prasanna, Ravi K Madduri, Joel Saltz, Dimitris Samaras

机构 * Stony Brook University（石溪大学） ； Argonne National Laboratory（阿贡国家实验室） ； The University of Chicago（芝加哥大学） ； University of Utah（犹他大学） 纠错

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

AI总结 PixCell是首个针对数字病理图像的生成基础模型，通过扩散模型在大规模数据集上训练，实现隐私保护的数据生成和虚拟染色任务，提升病理学研究效率。

Comments Project page - https://histodiffusion.github.io/docs/projects/pixcell

PathAgent: Toward Interpretable Analysis of Whole-slide Pathology Images via Large Language Model-based Agentic Reasoning

PathAgent: 通过基于大型语言模型的代理推理实现全切片病理图像的可解释分析

Jingyun Chen, Linghan Cai, Zhikang Wang, Yi Huang, Songhan Jiang, Shenjin Huang, Hongpeng Wang, Yongbing Zhang

机构 * Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）） ； Fudan University（复旦大学） ； Sichuan University（四川大学） 纠错

专题命中 病理影像 ：pathology(title)；分类 cs.CV

AI总结 PathAgent通过基于大型语言模型的代理推理，实现全切片病理图像的可解释分析，展现出强大的零样本泛化能力和临床应用潜力。

Comments 11 pages, 6 figures

MIDOG 2025 Track 2: A Deep Learning Model for Classification of Atypical and Normal Mitotic Figures under Class and Hardness Imbalances

Sujatha Kotte, Vangala Govindakrishnan Saipradeep, Vidushi Walia, Dhandapani Nandagopal, Thomas Joseph, Naveen Sivadasan, Bhagat Singh Lali

机构 * TCS Research, Tata Consultancy Services (TCS) Ltd（Tata Consultancy Services（TCS）有限公司研究部） ； Department of Pathology, Tata Medical Center（病理学部） 纠错

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

Comments MIDOG 2025 Track 2 submission

Multi-output Deep-Supervised Classifier Chains for Plant Pathology

Jianping Yao, Son N. Tran

机构 * School of Information and Communications Technology（信息与通信技术学院） ； University of Tasmania（塔斯马尼亚大学） 纠错

专题命中 病理影像 ：pathology(title)；分类 cs.CV

Journal ref 2023 International Joint Conference on Neural Networks (IJCNN)

Automated Multi-Class Crop Pathology Classification via Convolutional Neural Networks: A Deep Learning Approach for Real-Time Precision Agriculture

Sourish Suri, Yifei Shao

专题命中 病理影像 ：pathology(title)；分类 cs.CV

Comments 29 pages, 10 figures, 1 table. Code available at: https://github.com/Sourish85/CNN-CROP-DIS-DETECTOR