ERCPMP-Gx is a publicly available endoscopic, histopathological, and genomic dataset focused on colorectal polyposis. It contains 160 images and video clips from procedures performed with the Olympus EVIS X1 system, covering white‑light, narrow‑band, and magnifying imaging modes. The dataset links each record to standardized endoscopic annotations, representative histopathology, and clinically reported germline findings, with about 80 % of cases representing confirmed hereditary polyposis syndromes such as FAP, PJS, JPS, and GNS, and 20 % comprising non‑hereditary polyps or mimicking lesions for differential classification.
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:2609.14676v1 Announce Type: new
Abstract: Purpose: To compare four multimodal large language models (MLLMs) with radiologists of varying expertise in breast density assessment, BI-RADS assessme...
By Ali Abbasian Ardakani, Afshin Mohammadi, Taha Yusuf Kuzan, Beyza Nur Kuzan, Alisa Mohebbi, Masume Behruzi, Hamid Khorshidi, Ashkan Ghorbani, Elham Asadiara, Zeinab Khorshidi Lotfi, Ansar Rahman, Nedim Christoph Beste, U. Rajendra Acharya, Sepideh Hatamikia
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
arXiv:2608.28716v1 Announce Type: cross
Abstract: Deep learning segmentation models are often evaluated using geometric metrics such as Dice, HD95, and ASD, yet it remains unclear to what extent impr...
By Savvas Saragiotis, Pieter C. Gort, Lotte J. S. Fleurkens-Ewals, Anna F. van Herwijnen, Marion Tops-Welten, L. D. Kampmeijer, Joost Nederend, Fons van der Sommen
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.
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
The paper presents an automated segmentation pipeline for whole‑slide histopathology images of colorectal cancer, labeling tumor grades 1‑3 and normal mucosa. It employs dense prediction transformers with multiple encoder backbones, overlapping patches, test‑time augmentation, and an adaptive augmentation policy guided by large language models. The approach, combined with soft‑voting ensembles and post‑processing refinements, raises the F1 score from 62.92 to 69.84 on a colorectal cancer grade dataset.
By \"Umit Mert \c{C}a\u{g}lar, Alptekin Temizel
Colorectal cancer (CRC) is the second most deadly and third most common cancer, and the leading cause of death among gastrointestinal cancers. Early diagnosis is crucial for the treatment of this canc...
WILSON is a vision–language foundation model that represents whole‑slide images and multi‑slide patient cases as single multi‑magnification composite images. Trained on about 189,000 Mayo Clinic slides covering 42 organs and 829 diagnostic entities, it outperforms dedicated case‑level models on internal cohorts and matches slide‑level models while using far less compute. Fine‑tuning on triple‑negative breast cancer data improves histologic subtyping and lymphocyte grading, and the model retrieves diagnostic text with high recall and generates captions closer to report references than prior methods.
By Saghir Alfasly, Wataru Uegami, Sobhan Hemati, Wenchao Han, Xiaojia Tang, Kevin Thompson, Daniel Stone, Ghazal Alabtah, Saba Yasir, Michael R. Lucas, Eric W. Klee, Cheryl L. Willman, Judy C. Boughey, Matthew P. Goetz, Krishna R. Kalari, H. R. Tizhoosh
arXiv:2607.19261v4 Announce Type: replace-cross
Abstract: Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating...
By Dankai Liao, Tianyi Zhang, Yufeng Wu, Xinyue Zhang, Qiaochu Xue, Zeyu Liu, Dachun Zhao, Linghan Cai, Yueming Jin
arXiv:2606. 24115v1 Announce Type: cross Abstract: Vision-language models (VLMs) are prone to hallucination, which remains a major barrier to their safe deployment in clinical practice.
By Aminu Lawal, Niyoj Oli, Sachin Acharya, Prashnna Gyawali, Maria Carmen Romano, Binod Bhattarai