Whole slide images (WSIs) provide rich diagnostic information for computational pathology, but their gigapixel scale, stain variation, scanner differences, tissue artifacts, and limited expert annotation make robust model training challenging. This paper presents a multi-source Masked Autoencoder (MAE) framework, named ProsMAE, for histopathology representation learning.
HERO (Histology Encoder for Robust Representation in Oncology) is a ViT‑G/14 pathology foundation model trained with DINO and iBOT objectives and refined using high‑resolution Gram anchoring on a 500‑million‑tile corpus from about 575,000 clinical whole‑slide images. It demonstrates superior robustness to center, scanner, and stain variation compared to other state‑of‑the‑art foundation models, while maintaining competitive performance on tile‑level classification, segmentation, and gene‑expression prediction. Across 39 slide‑level clinical tasks, HERO ranks first on average and achieves the best average rank across six benchmark frameworks under an equal‑weighted analysis.
By Zhi Li (Caris Life Sciences, Irving, TX, United States), Eghbal Amidi (Caris Life Sciences, Irving, TX, United States), Yating Cheng (Caris Life Sciences, Irving, TX, United States), Tyson Dawson (Caris Life Sciences, Irving, TX, United States), Gorkem Can Ates (Caris Life Sciences, Irving, TX, United States), Shuzhen Kuang (Caris Life Sciences, Irving, TX, United States), Norsang Lama (Caris Life Sciences, Irving, TX, United States), Md Ashequr Rahman (Caris Life Sciences, Irving, TX, United States), Zhiying Lu (Caris Life Sciences, Irving, TX, United States), Elisabeth K. Kong (Caris Life Sciences, Irving, TX, United States), Milan Radovich (Caris Life Sciences, Irving, TX, United States), David Spetzler (Caris Life Sciences, Irving, TX, United States), Matthew Oberley (Caris Life Sciences, Irving, TX, United States), George W. Sledge (Caris Life Sciences, Irving, TX, United States), Ming Chen (Caris Life Sciences, Irving, TX, United States)
The paper introduces ANT, a test‑time adaptation framework that improves prostate cancer detection in micro‑ultrasound by performing a segmentation‑guided adaptation. ANT aligns a pretrained detection encoder to the target domain’s prostate anatomy using pseudo‑masks from a frozen segmentation network, thereby correcting domain‑specific feature drift while preserving cancer‑discriminative features. In a leave‑one‑center‑out evaluation, ANT raises mean AUC by 2.9% at the biopsy‑core level and 3.6% at the patient level compared to no adaptation, outperforming existing TTA baselines.
By Obed Korshie Dzikunu, Mohammad Mahdi Abootorabi, Mohamed Harmanani, Paul F. R. Wilson, Emma Willis, Ferdinand Luger, Adam Kinnaird, Brian Wodlinger, Parvin Mousavi, Purang Abolmaesumi
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
arXiv:2607. 18218v1 Announce Type: cross Abstract: Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data.
By Naoto Usuyama, Jeya Maria Jose Valanarasu, Sicong Yao, Hanwen Xu, Jaspreet Bagga, Guanghui Qin, Robert E. Kramer, Cliff Wong, Soohee Lee, Hao Qiu, Theodore Zhengde Zhao, Racheli Ben Shimol, Angela Crabtree, Kevin Matlock, Eduardo Alejandro Lozano Garcia, Naiteek Sangani, Alberto Santamaria-Pang, Jason Entenmann, Alexandra Q. Bartlett, Bill J. Wright, Bernard A. Fox, Brian Piening, Sheng Zhang, Sheng Wang, Tristan Naumann, Carlo Bifulco, Hoifung Poon
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...