arXiv:2601.03163v2 Announce Type: replace
Abstract: Background and Objective: Precise and scalable instance segmentation of cell nuclei is a fundamental prerequisite for computational pathology, yet...
By Mat\v{e}j Pek\'ar, V\'it Musil, Rudolf Nenutil, Petr Holub, Tom\'a\v{s} Br\'azdil
arXiv:2609.24560v1 Announce Type: new
Abstract: Confocal Laser Endomicroscopy (CLE) provides real-time, cellular-resolution optical biopsy but has a narrow field of view, which image mosaicing can ex...
By Ahmed Aboelela, Johannes Barcsay, Jana Friedhof, Miguel Gon\c{c}alves, Alexander Hann, Katharina Breininger
Domain Elastic Transform (DET) is a grid‑free, probabilistic framework that jointly aligns geometry and high‑dimensional vector‑valued functions on irregular sparse manifolds, such as those found in spatial transcriptomics. By treating data as functions rather than voxelized images, DET performs unsupervised, scalable registration through sampled point alignment and displacement interpolation, guided by a joint spatial‑functional Bayesian likelihood. Evaluations on MERFISH mouse‑brain slices and Stereo‑seq mouse‑embryo atlases show DET achieving superior spatial overlap and topology compared to existing pipelines, with an accelerated variant delivering high label‑transfer accuracy.
By Osamu Hirose, Emanuele Rodola
arXiv:2606. 03644v1 Announce Type: new Abstract: Comprehensive molecular profiling is essential for modern precision oncology but remains hindered by prohibitive costs, specimen exhaustion, and protracted turnaround times.
By Fengtao Zhou, Yingxue Xu, Zhengyu Zhang, Yihui Wang, Zhengrui Guo, Ling Liang, Jiabo Ma, Cheng Jin, Ziyi Liu, Huajun Zhou, Hongyi Wang, Du Cai, Chenglong Zhao, Xi Wang, Can Yang, Yu Wang, Wenbin Li, Feng Gao, Zhe Wang, Zhenhui Li, Xiuming Zhang, Li Liang, Hao Chen
arXiv:2608.24364v1 Announce Type: new
Abstract: Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic underst...
By Sebasti\'an Gonz\'alez, Karen Sanchez, Jos\'e M. Saavedra, Marcelo Pizarro, Bernard Ghanem
CellPath-Bench is a new benchmark that evaluates whole-slide cellular representations in pathology foundation models (PFMs) by using 25 spatially aligned H&E–Xenium tissue sections from 11 organs and over 7 million cells. It introduces metrics such as Cell Representation Advantage (CRA) and Cell Representation Transferability (CRT) to assess how well frozen PFMs encode cell-type information and generalize across tissue sections, datasets, and organs. The benchmark was applied to 30 PFMs, revealing significant model-dependent differences in cell-type decodability and cross-domain generalization, and offers a standardized framework for auditing cellular information in frozen PFM representations.
By Bokai Zhao, Yiyang Zhang, Hanqing Chao, Yawei Ma, Long Bai, Tai Ma, Minfeng Xu, Ming Song, Tianzi Jiang