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BatchBench: Benchmarking AI Robustness to Batch Effects and Domain Shift in Biomedical Images - A JUMP Cell Painting Leaderboard

Sonja Topf, Ana Sanchez-Fernandez, Gnter Klambauer · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30104 · cited by 0 works (OpenAlex) · open-access PDF

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A pesar de los avances recientes, los sistemas de IA actuales siguen siendo altamente susceptibles a cambios de dominio, especialmente en la imagen biomédica, donde las imágenes generalmente se adquieren bajo condiciones experimentales sustancialmente diferentes. Aunque los métodos de adaptación de dominios recientes han logrado un progreso sustancial, persisten diferencias significativas de rendimiento durante el cambio de dominio. Para medir el progreso en este sentido, presentamos BatchBench, un tablero líder que evalúa y compara la robustez de los sistemas de IA en imágenes biomédicas de lotes experimentales y fuentes de datos invisibles. BatchBench está construido sobre JUMP-CP, uno de los mayores conjuntos de datos de imagen biomédica disponibles públicamente. Generado utilizando el ensayo de pintura celular, JUMP-CP exhibe efectos

Machine translation (Meta M2M100); the English original follows.

Abstract

Despite recent advances, current AI systems remain highly susceptible to domain shifts, particularly in biomedical imaging, where images are usually acquired under substantially different experimental conditions. Although recent domain adaptation methods have made substantial progress, significant performance gaps under domain shift persist. To measure progress in this regard, we introduce BatchBench, a leaderboard that evaluates and compares the robustness of AI systems on biomedical images from unseen experimental batches and data sources. BatchBench is built on JUMP-CP, one of the largest publicly available biomedical imaging datasets. Generated using the Cell Painting assay, JUMP-CP exhibits pronounced batch effects and domain shifts, making it a challenging testbed for evaluating robustness to distributional changes.

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Topics: Robustness (evolution), Computer science, Benchmarking, Artificial intelligence, Testbed, Domain adaptation

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