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Towards Large-Scale Training of Pathology Foundation Models
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Driven by the recent advances in deep learning methods and, in particular, by the development of modern self-supervised learning algorithms, increased interest and efforts have been devoted to build foundation models (FMs) for medical images. In this work, we present our scalable training pipeline for large pathology imaging data, and a comprehensive analysis of various hyperparameter choices and training techniques for building pathology FMs. We release and make publicly available the first batch of our pathology FMs (https://github.com/kaiko-ai/towards_large_pathology_fms) trained on open-access TCGA whole slide images, a commonly used collection of pathology images. The experimental evaluation shows that our models reach state-of-the-art performance on various patch-level downstream tasks, ranging from breast cancer subtyping to colorectal nuclear segmentation. Finally, to unify the evaluation approaches used in the field and to simplify future comparisons of different FMs, we present an open-source framework (https://github.com/kaiko-ai/eva) designed for the consistent evaluation of pathology FMs across various downstream tasks.
Forward citations
Cited by 8 Pith papers
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Towards Robust Foundation Models for Digital Pathology
PathoROB shows that all 20 evaluated pathology foundation models encode medical center information and that lower robustness correlates with larger downstream performance drops.
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Current Pathology Foundation Models are unrobust to Medical Center Differences
Ten pathology foundation models encode medical center identity more strongly than cancer type in their embeddings, according to a new Robustness Index.
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GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
Distilled, Apache-2.0-licensed GigaPath-Flash and GigaTIME-Flash models deliver most of the original models' accuracy at a fraction of the compute and memory.
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Atlas 2 -- Foundation models for clinical deployment
Atlas 2 and its distilled variants set new average state-of-the-art results across 80 pathology benchmarks, with larger robustness margins over prior models.
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Reusable specimen-level inference in computational pathology
SpinPath packages pretrained specimen-level pathology models with Python and browser inference tools and benchmarks them on an external breast cancer metastasis dataset.
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Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit\'e, and Aignostics
Atlas, a 632M-parameter ViT pathology model trained on 1.2M multi-stain slides, achieves a 61.9 percent average on 21 public benchmarks, the best among seven leading foundation models.
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Are the Latent Representations of Foundation Models for Pathology Invariant to Rotation?
Rotated pathology image patches produce more similar latent representations in models that used rotation augmentation during self-supervised training.
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A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks
A systematic literature review of computational pathology foundation models that catalogs datasets, SSL adaptation strategies, and evaluation tasks into taxonomies.
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