[2601.03811] EvalBlocks: A Modular Pipeline for Rapidly Evaluating Foundation Models in Medical Imaging

Computer Science > Computer Vision and Pattern Recognition arXiv:2601.03811 (cs) [Submitted on 7 Jan 2026 (v1), last revised 1 Apr 2026 (this version, v2)] Title:EvalBlocks: A Modular Pipeline for Rapidly Evaluating Foundation Models in Medical Imaging Authors:Jan Tagscherer, Sarah de Boer, Lena Philipp, Fennie van der Graaf, Dré Peeters, Joeran Bosma, Lars Leijten, Bogdan Obreja, Ewoud Smit, Alessa Hering View a PDF of the paper titled EvalBlocks: A Modular Pipeline for Rapidly Evaluating Foundation Models in Medical Imaging, by Jan Tagscherer and 9 other authors View PDF HTML (experimental) Abstract:Developing foundation models in medical imaging requires continuous monitoring of downstream performance. Researchers are burdened with tracking numerous experiments, design choices, and their effects on performance, often relying on ad-hoc, manual workflows that are inherently slow and error-prone. We introduce EvalBlocks, a modular, plug-and-play framework for efficient evaluation of foundation models during development. Built on Snakemake, EvalBlocks supports seamless integration of new datasets, foundation models, aggregation methods, and evaluation strategies. All experiments and results are tracked centrally and are reproducible with a single command, while efficient caching and parallel execution enable scalable use on shared compute infrastructure. Demonstrated on five state-of-the-art foundation models and three medical imaging classification tasks, EvalBlocks stream...