arXiv:2609.00396v1 Announce Type: new
Abstract: Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervis...
By Chad Wong, Sicheng Chen, Tianyi Zhang, Enhui Chai, Yueming Jin, Zeyu Liu, Fei Xia
arXiv:2507. 05077v5 Announce Type: replace-cross Abstract: Deep neural networks are increasingly applied in automated histopathology.
By Tarun Gogisetty, Naman Malpani, Gugan Thoppe, Sridharan Devarajan
The paper introduces a pipeline that uses publicly available whole slide image foundation models (FMs) to automatically triage slides by ranking them based on zero‑shot classification predictions. This approach accurately identifies slides containing the most tumor, achieving top‑2 ranking for patients with up to 43 slides across multiple datasets. The study also proposes a ranked evaluation framework to benchmark FM performance in slide triage.
By Ayushi Sinha, Shashank Yadav, Benjamin Holmes, Pravat Das, Aaron W. Bogan, James S. Lewis Jr., Santiago Romero-Brufau, Andrew Y. K. Foong, Scott H. Kaufmann, Kathryn M. Van Abel, David M. Routman, Michael R. Lucas
arXiv:2505.03380v2 Announce Type: replace
Abstract: Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains...
By Haonan Wang, Jiaji Mao, Lehan Wang, Qixiang Zhang, Marawan Elbatel, Yi Qin, Huijun Hu, Baoxun Li, Wenhui Deng, Weifeng Qin, Hongrui Li, Jialin Liang, Jun Shen, Xiaomeng Li
arXiv:2608.30420v1 Announce Type: cross
Abstract: Automating the analysis of whole-slide images has high clinical value, since characterizing cancers requires examining them in detail. Such analysis...
By Tiffanie Godelaine, Maxime Zanella, Karim El Khoury, Benoit Macq, Christophe De Vleeschouwer
The paper introduces RA-CMF, a Region‑Adaptive Conditional MeanFlow framework for CT image reconstruction. It combines a conditional MeanFlow network that predicts flow fields for image refinement with a reinforcement‑learning driven policy that allocates tile‑wise refinement budgets. The method achieves high reconstruction quality, reporting a tumor ROI radiomic feature CCC of 0.93 ± 0.09, PSNR of 31.94 ± 2.64, SSIM of 0.97 ± 0.03, and overall PSNR of 34.23 ± 1.71 and SSIM of 0.95 ± 0.01.
By Md Shifatul Ahsan Apurba, Md Selim, Jin Chen