GigaPath-Flash和GigaTIME-Flash：高效病理基础模型用于全切片和肿瘤微环境分析
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
研究者发布了两个轻量级病理AI模型，GigaPath-Flash在切片分析上几乎不输十亿参数大模型，速度快50倍；GigaTIME-Flash还能直接预测肿瘤微环境，速度更快又省显存。开源可用，做病理和免疫研究很实用。
GigaPath-Flash结合22M参数的ViT-S tile编码器和21M参数的LongNet slide编码器，从十亿参数的GigaPath蒸馏而来，保留97%平均切片级性能但计算量减少50倍。
GigaTIME-Flash可直接从H&E图像预测肿瘤免疫微环境，比原版CNN的GigaTIME快6倍、GPU内存少8倍。两个模型均采用Apache-2.
0开源许可，权重开放。
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data. A growing landscape of pathology foundation models now spans diverse data sources, architectures, and downstream applications. However, most pretrained models operate only at the image-tile level, use restrictive licenses, and remain computationally expensive, limiting large-scale slide-level clinical and research use. Here, we introduce GigaPath-Flash and GigaTIME-Flash, efficient models for whole-slide pathology AI and spatial proteomics prediction. GigaPath-Flash combines a 22M-parameter ViT-S tile encoder with a 21M-parameter LongNet slide encoder, both pretrained on large-scale real-world histopathology data. Its compact tile encoder is distilled from the billion-parameter GigaPath (ViT-g) teacher and shared by both models. GigaPath-Flash retains 97% of GigaPath's average slide-level performance with 50x less compute. GigaTIME-Flash extends this backbone to predict the tumor immune microenvironment directly from routine H&E images. It surpasses the original CNN-based GigaTIME in prediction quality while running 6x faster and using 8x less GPU memory. Together with GigaPath and GigaTIME, these models form an open-weight, Apache-2.0-licensed family pretrained on large-scale real-world clinical data. By releasing all models and weights, we provide accessible building blocks for computational pathology, immuno-oncology, and precision health.
This report is categorized under AITOP's "Models" desk. Key entities and technical topics under continuous tracking include GigaPath-Flash, GigaTIME-Flash, 病理学基础模型, 肿瘤微环境.
Source attribution: curated from arXiv cs.AI (@Naoto Usuyama, Jeya Maria Jose Valanarasu, Sicong Yao, Hanwen Xu, Jaspreet Bagga, Guanghui Qin, Robert E. Kramer, Cliff Wong, Soohee Lee, Hao Qiu, Theodore Zhengde Zhao, Racheli Ben Shimol, Angela Crabtree, Kevin Matlock, Eduardo Alejandro Lozano Garcia, Naiteek Sangani, Alberto Santamaria-Pang, Jason Entenmann, Alexandra Q. Bartlett, Bill J. Wright, Bernard A. Fox, Brian Piening, Sheng Zhang, Sheng Wang, Tristan Naumann, Carlo Bifulco, Hoifung Poon).
