arXiv Computer Vision

FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation

arXiv AI
3d ago

Towards Accurate and Lightweight Peripheral Neuroblastic Tumor Diagnosis via Contrastive Multi-scale Pathological Image Analysis

The paper introduces CoPath, a lightweight framework for diagnosing peripheral neuroblastic tumors (pNTs) from whole-slide images. CoPath combines CoHisNet, a multi‑scale feature‑fusion network that replaces traditional MLPs with Kolmogorov‑Arnold Network layers for efficient nonlinear modeling, and PathVote, which aggregates patch‑level predictions using pathology‑informed priors. Experiments on a private pNT cohort and the public BreakHis dataset show that CoPath matches or surpasses existing classifiers while reducing computational complexity.

By Zhu Zhu, Shuo Jiang, Jingyuan Zheng, Yawen Li, Yifei Chen, Manli Zhao, Weizhong Gu, Feiwei Qin, Jinhu Wang, Gang Yu
arXiv AI
Jun 17

SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology

arXiv:2606. 17702v1 Announce Type: cross Abstract: Characterising the tumour microenvironment (TME) from routine H&E-stained histology images requires simultaneous cell segmentation, feature extraction, and interpretable clinical reporting.

By Wan Siti Halimatul Munirah Wan Ahmad, Faris Syahmi Samidi, Mohammad Badal Ahmmed, Vimal Angela Thiviyanathan, Selvam James Thavaraj, Anwar P. P. Abdul Majeed
arXiv AI
Aug 18

Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology

arXiv:2608. 15915v1 Announce Type: cross Abstract: Lung cancer remains the leading cause of cancer-related mortality worldwide, while histopathological diagnosis is often affected by inter-observer variability and the substantial workload associated with manual slide examination.

By Hadi Hasan, Safaa Salman, Lama Sleem, Ralph Mouawad, Ali Chehab
arXiv AI
Jun 8

Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge

arXiv:2606. 07368v1 Announce Type: cross Abstract: Automated mitosis detection is a well-established task in computational pathology.

By Marc Aubreville, Jonas Ammeling, Sweta Banerjee, Viktoria Weiss, Taryn A. Donovan, Robert Klopfleisch, Jiaqi Lv, Shan E Ahmed Raza, Rapha\"el Bourgade, Thomas Walter, Yasemin Topuz, Song\"ul Varl{\i}, Charles-Antoine Collins-Fekete, Zhuoyan Shen, Navya Sri Kelam, Nitin Singhal, Christian Marzahl, Brian Napora, Tengyou Xu, Hongyan Gu, Mario Vento, Gennaro Percannella, Norbert Ropiak, Izabela Wasiak, Jie Xiao, Shaojun Liu, Seungho Choe, April Khademi, Vidushi Walia, Sujatha Kotte, Andrew Broad, Alex Wright, Guillaume Balezo, Esha Sadia Nasir, Mostafa Jahanifar, Yosuke Yamagishi, Shouhei Hanaoka, Mattia Sarno, Francesco Tortorella, Biwen Meng, Jingxin Liu, Sara Krauss, Daniel Hieber, Lavish Ramchandani, Dev Kumar Das, Mieko Ochi, Yuan Bae, Piotr Giedziun, Mateusz Maniewski, Vangala Govindakrishnan Saipradeep, Naveen Sivadasan, Leire Benito-Del-Valle, Adrian Galdran, Kaustubh Atey, Sameer Anand Jha, Adinath Dukre, Imran Razzak, Maxime W. Lafarge, Viktor H. Koelzer, Nils Porsche, Nikolas Stathonikos, Mitko Veta, Dominik Hirling, Zsanett Zs\'ofia Iv\'an, Peter Horvath, Katharina Breininger, Christof A. Bertram