arXiv AI By Hikmat Khan, Metin N. Gurcan, Anil Parwani, Wendy L. Frankel, Wei Chen, Muhammad Khalid Khan Niazi

Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images

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arXiv AI
Jun 8

DaX: Learning General Pathology Representations Across Scales

arXiv:2606. 06983v1 Announce Type: cross Abstract: Computational pathology requires visual representations that transfer across diverse clinical endpoints and remain robust to variation in magnification, staining, scanner type, slide preparation, and input resolution.

By Bokai Zhao, Yiyang Zhang, Long Bai, Tai Ma, Hanqing Chao, Minfeng Xu
arXiv AI
Oct 1

Colorectal Cancer Segmentation with Adaptive Augmentation and Multi-Resolution Ensemble Models

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 AI
Aug 5

SAGE: Semantic Explainability of Attention-Based Survival Models in Computational Pathology

arXiv:2608. 02803v1 Announce Type: cross Abstract: Attention-based multiple instance learning (ABMIL) is the predominant approach for slide-level prediction in computational pathology, yet its attention maps provide only local explanations: they indicate where a model focuses but not which histological features drive its predictions or how the model behaves across a patient cohort.

By Abdallah Lamane, Abdul Rahman Diab, Ren-Chin Wu, William Lotter