GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis

• URL: http://arxiv.org/abs/2607.18218v2
• Date: Wed, 22 Jul 2026 19:34:58 GMT
• Title: GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
• Abstract summary: Foundation models can transform cancer diagnosis, prognosis, and treatment selection.<n>Most pretrained models operate only at the image-tile level, use restrictive licenses, and remain computationally expensive.<n>Here, we introduce GigaPathFlash and GigaTIME-Flash, efficient models for whole-slide pathology AI and spatial prediction.
• Score: 40.470643969281106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Graph-Based Framework for Interpretable Whole Slide Image Analysis [86.37618055724441]
  We develop a framework that transforms whole-slide images into biologically-informed graph representations.<n>Our approach builds graph nodes from tissue regions that respect natural structures, not arbitrary grids.<n>We demonstrate strong performance on challenging cancer staging and survival prediction tasks.
  arXiv  Detail & Related papers  (2025-03-14T20:15:04Z)
• Predictable Scale: Part I, Step Law -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining [59.369484219304866]
  We conduct an unprecedented empirical investigation training over 3,700 Large Language Models (LLMs) from scratch across 100 trillion tokens.<n>We establish a universal Scaling Law for hyperparameter optimization in LLM Pre-training, called Step Law.<n>Our estimated optima deviates from the global best performance found via exhaustive search by merely 0.094% on the test set.
  arXiv  Detail & Related papers  (2025-03-06T18:58:29Z)
• Recurrent Diffusion for Large-Scale Parameter Generation [52.98888368644455]
  We introduce Recurrent Diffusion for Large Scale Generation (RPG), a novel framework that generates full neural network parameters up to hundreds of millions on a single GPU.<n>RPG serves as a critical advance in AI generating AI, potentially enabling efficient weight generation at scales previously deemed infeasible.
  arXiv  Detail & Related papers  (2025-01-20T16:46:26Z)
• PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysis [9.862551438475666]
  We propose a novel top-down method for hierarchical weakly supervised representation learning on slide-level tasks in computational pathology.<n>PATHS is inspired by the cross-magnification manner in which a human pathologist examines a slide, filtering patches at each magnification level to a small subset relevant to the diagnosis.<n>We apply PATHS to five datasets of The Cancer Genome Atlas (TCGA), and achieve superior performance on slide-level prediction tasks.
  arXiv  Detail & Related papers  (2024-11-27T11:03:38Z)
• Phikon-v2, A large and public feature extractor for biomarker prediction [42.52549987351643]
  We train a vision transformer using DINOv2 and publicly release one iteration of this model for further experimentation, coined Phikon-v2. While trained on publicly available histology slides, Phikon-v2 surpasses our previously released model (Phikon) and performs on par with other histopathology foundation models (FM) trained on proprietary data.
  arXiv  Detail & Related papers  (2024-09-13T20:12:29Z)
• LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation [2.0901574458380403]
  We propose a new lightweight but efficient model, namely LiteNeXt, for medical image segmentation.<n>The model is trained from scratch with small amount of parameters (0.71M) and Giga Floating Point Operations Per Second (0.42).<n>Experiments on public datasets including Data Science Bowls, GlaS, ISIC2018, PH2, Sunnybrook, and Lung X-ray data show promising results.
  arXiv  Detail & Related papers  (2024-04-04T01:59:19Z)
• WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images [5.960501267687475]
  We investigate how to generate pathology reports given whole slide images. We curated the largest WSI-text dataset (PathText) On the model end, we propose the multiple instance generative model (MI-Gen)
  arXiv  Detail & Related papers  (2023-11-27T05:05:41Z)
• Quilt-1M: One Million Image-Text Pairs for Histopathology [9.891861444518605]
  We use YouTube to curate a vision-language dataset consisting of $802, 144$ image and text pairs.<n>We combine QUILT with datasets from other sources, including Twitter, research papers, and the internet in general, to create QUILT-1M.<n>Our model outperforms state-of-the-art models on both zero-shot and linear probing tasks for classifying new histopathology images.
  arXiv  Detail & Related papers  (2023-06-20T00:14:47Z)
• Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics [63.76637479503006]
  Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. This paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability.
  arXiv  Detail & Related papers  (2022-11-29T23:47:56Z)
• M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining [55.16088793437898]
  Training extreme-scale models requires enormous amounts of computes and memory footprint. We propose a simple training strategy called "Pseudo-to-Real" for high-memory-footprint-required large models.
  arXiv  Detail & Related papers  (2021-10-08T04:24:51Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.