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:2608. 03990v1 Announce Type: new Abstract: Synthetic histopathology image generation has emerged as an approach that may address data scarcity in computational pathology, yet current evaluation methodologies may not fully assess synthetic data quality for medical applications.
By Seyed Kahaki, Shijie Li, Weijie Chen, Nicholas Petrick
arXiv:2610.18243v1 Announce Type: cross
Abstract: Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportun...
By Hao Zhang, Yi Huang, Wei Liu, Yuan Gao, Zeju Li, Xu Han, Yu Shi, Ling Zhang, Ke Yan
LiFUSE introduces a unified frequency‑spatial self‑supervised pretraining framework for liver disease analysis on non‑contrast CT scans. The method employs a frequency branch that uses random band‑stop masking and spectrum reconstruction, and a spatial branch that performs hybrid‑granularity masked volumetric reconstruction, with a cross‑domain consistency scheme to fuse the two branches. Experiments on three clinical tasks and a public transfer setting demonstrate that LiFUSE consistently outperforms strong baselines, indicating its potential to enhance liver disease screening using low‑contrast NCCT images.
The study introduces dLI-RADS, an interpretable AI system that estimates hepatocellular carcinoma probability from multiphase CT images of liver lesions while aligning with LI-RADS imaging concepts. By combining neural network predictions with handcrafted quantitative imaging features and logistic regression, dLI-RADS achieved 80% lesion-level accuracy, outperforming standard LI-RADS (74%) in a multicenter retrospective cohort. The method was validated using bootstrap non‑inferiority tests and external sensitivity comparisons.
By Emma Sarfati, Alexandre B\^one, Pietro Gori, Sisi Yang, Marc-Michel Roh\'e, Fran\c{c}ois Nicolas, Jeong-Min Lee, Jeong Hee Yoon, Maxime Ronot, Isabelle Bloch, Christophe Aub\'e
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