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
The paper introduces CoPath, a lightweight framework for diagnosing peripheral neuroblastic tumors (pNTs) from whole-slide images. CoPath combines CoHisNet, a multi‑scale feature‑fusion network that replaces traditional MLPs with Kolmogorov‑Arnold Network layers for efficient nonlinear modeling, and PathVote, which aggregates patch‑level predictions using pathology‑informed priors. Experiments on a private pNT cohort and the public BreakHis dataset show that CoPath matches or surpasses existing classifiers while reducing computational complexity.
By Zhu Zhu, Shuo Jiang, Jingyuan Zheng, Yawen Li, Yifei Chen, Manli Zhao, Weizhong Gu, Feiwei Qin, Jinhu Wang, Gang Yu
arXiv:2604. 27277v3 Announce Type: replace-cross Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data.
By Yizhou Wu, Shansong Wang, Yuheng Li, Mojtaba Safari, Mingzhe Hu, Chih-Wei Chang, Harini Veeraraghavan, Xiaofeng Yang
PathoHR is a new pipeline for predicting breast cancer survival from high‑resolution pathological images. It uses a plug‑and‑play Vision Transformer to enhance patch‑wise whole slide image representations, evaluates multiple similarity metrics to optimize feature learning, and shows that smaller, enhanced patches can match or surpass the accuracy of larger raw patches while cutting computational cost. The authors provide experimental evidence that this approach improves both accuracy and efficiency in computational pathology.
By Yang Luo, Shiru Wang, Jun Liu, Jiaxuan Xiao, Rundong Xue, Zeyu Zhang, Hao Zhang, Yu Lu, Yang Zhao, Yutong Xie
The paper presents ORB-SVM, a hybrid framework that combines the ORB algorithm for feature extraction with a Support Vector Machine for classifying brain tumors in MRI scans. It achieves a 99.5% reduction in data size while preserving key diagnostic features, and reports a 97.5% classification accuracy on the Br35H dataset. This approach offers a resource‑efficient alternative to deep learning models, reducing computational cost and data requirements.
By Amirhosein Azarpour
arXiv:2609.18578v1 Announce Type: new
Abstract: Pathologists diagnose diseases by first locating suspicious tissue and then examining it at higher magnification, whereas self-supervised vision transf...
By Anabel Stammer, Valay Bundele, Mehran Hosseinzadeh, Hendrik P. A. Lensch