ERCPMP-Gx is a publicly available dataset that combines endoscopic images and videos with histopathological slides and germline genetic data to characterize colorectal polyposis. It includes 160 images and video clips from procedures performed with various white‑light and narrow‑band imaging modes, and covers about 80% hereditary polyposis syndromes such as FAP, PJS, JPS, and GNS, with the remaining 20% representing non‑hereditary or mimicking lesions. Each record is linked to standardized endoscopic annotations, representative histopathology, and clinically reported germline findings, creating an AI‑ready, patient‑level annotation framework.
By Zahra Ghaffari, Massih Bahar, Mojgan Forootan, Ali Darvishi, Hamidreza Bolhasani
arXiv:2608. 07543v1 Announce Type: cross Abstract: Background and Study Aims: Accurate optical diagnosis of colorectal polyps guides resection strategy and surveillance, with multimodal large language models (MLLMs) showing potential for image-based diagnosis.
By Joshua C. Vences, William T. Tran, Nikko Gimpaya, Catharine M. Walsh, Rishad J. Khan, Robert Bechara, Asher C. Wiggins, Celine N. Rousan, Kaitlyn V. G. L. Morgado, Angie Ibrahim, Kevin H. M. Kuo, Daniel von Renteln, Alexander Hann, Dennis L. Shung, Michael A. Scaffidi, Charles M\'enard, Joshua Landy, Samir C. Grover
arXiv:2607. 03253v1 Announce Type: cross Abstract: As hematoxylin & eosin (H&E) staining constitutes the primary entry point in routine diagnostic workflows, computer-aided diagnosis from whole-slide H&E images is of particular clinical relevance.
By Ivica Kopriva, Dario Sitnik, Arijana Pacic, Karolina Krstanac, Irena Veliki Dalic, Marijana Popovic Hadzija
arXiv:2607.12750v3 Announce Type: replace
Abstract: Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally, with approximatel...
By Elham Amjadi, Amin Bahreini, Sayed Mohammad Hasan Emami, Sayyed Mohammadreza Hakimian, Alireza Fahim, Hojjatollah Rahimi, Hamidreza Bolhasani
Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally, with approximately 1,926,425 new cases and 904,019 deaths reported in 2022. Accurate histologic grading plays a critical role in prognosis and treatment planning for colorectal adenocarcinoma.
SegCol is a new dataset and benchmark for semantic segmentation of colon fold edges and surgical instruments in colonoscopy images, derived from the EndoMapper dataset. It offers manually annotated pixel‑level masks for three instrument classes and thin fold‑edge structures across temporally consistent image sequences, and serves as the basis for the SegCol Challenge within the EndoVis Challenge at MICCAI 2024. The study evaluates supervised segmentation and annotation‑efficient active learning, analyzes various segmentation metrics under structural perturbations, and highlights how metric behavior depends on target structure, underscoring the need for carefully selected evaluation protocols in endoscopic segmentation.
By Xinwei Ju, Rema Daher, Razvan Caramalau, Baoru Huang, Danail Stoyanov, Francisco Vasconcelos
arXiv:2609.15130v1 Announce Type: cross
Abstract: woma is a real-time foundation model for gastrointestinal endoscopy: a network trained without labels on about a million endoscopy frames, from which...
By Thang Tran, Lan Dang
Recent advances in pathology foundation models have enabled accurate prediction of spatial transcriptomics (ST) from routine H&E images. However, existing explainability methods for vision transformer...
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:2602. 04819v5 Announce Type: replace-cross Abstract: Accurate risk stratification of precancerous polyps during routine colonoscopy screening is a key strategy to reduce the incidence of colorectal cancer (CRC).
By Aqsa Sultana, Rayan Afsar, Ahmed Rahu, Surendra P. Singh, Brian Shula, Brandon Combs, Derrick Forchetti, Vijayan K. Asari
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
arXiv:2610.00414v1 Announce Type: new
Abstract: Foundation models pretrained on large-scale datasets demonstrate strong transferability to medical imaging tasks. However, understanding how their late...
By Michael D. Vasilakakis (Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece), Dimitris K. Iakovidis (Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece)