arXiv AI
Aug 24

CellPath-Bench: A Multidimensional Benchmark for Whole-Slide Cellular Representations in Pathology Foundation Models

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
arXiv AI
Jul 28

scMIR: a vision-language foundation model for single-cell light microscopy image representation

arXiv:2607. 22712v1 Announce Type: cross Abstract: Single-cell light microscopy images have become an important data source for characterizing cell phenotypes, but their complexity and heterogeneity pose challenges to high-throughput automated analysis.

By Yifan Shang (Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China, College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Jiahui Tan (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Xiangxiang Zeng (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Renjie Zhou (Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China)
arXiv Computer Vision
Sep 21

Catena: A Comprehensive Software Suite for Large-Scale Connectomics

Catena is an open‑source, developer‑centric software suite designed to streamline large‑scale connectomics from electron microscopy data. It integrates modules for 3D neuron and organelle segmentation, synapse detection, microtubule tracking, and neurotransmitter inference into composable, chunk‑wise pipelines that are fully documented and extensible. The suite includes pretrained machine learning models, containerized runtimes, and shareable components to reduce compute and ground‑truth data needs while ensuring reproducible, scalable processing across workstations and clusters.

By Samia Mohinta, Pedro G\'omez-G\'alvez, Shi Yan Lee, Daniel Franco-Barranco, Michael Clayton, Stephan Preibisch, Jan Funke, Albert Cardona