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Abstract Foundation models (FMs) using self-supervised pre-training are widely advocated as a universal solution for pathology, yet their advantages over task-specific (TS) models remain unclear. Here we show, using over 100,000 prostate biopsies from 6268 patients across 15 sites, that while FMs demonstrate utility in data-scarce scenarios, their performance converges with—and is in some cases surpassed by—TS models with sufficient labeled training data.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
