Batch Effect Correction

Latest papers

9 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

BatchBench: Benchmarking AI Robustness to Batch Effects and Domain Shift in Biomedical Images - A JUMP Cell Painting Leaderboard
Oct 21, 2026Sonja Topf, Ana Sanchez-Fernandez, Günter KlambauerDistribution Shift Robustness Medical Image Benchmarks
ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University, Linz, Austria · Clinical Research Institute for Medical AI, Johannes Kepler University, Linz, Austria
Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification
Sep 23, 2026Ilán Carretero, Pablo Meseguer, Rocío del Amor +1Batch Effect Correction Pathology Foundation Models
CVBLab, HumanTech, Universitat Politècnica de València (UPV), Valencia, Spain · Artikode Intelligence S.L., Valencia, Spain
Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation
Jun 27, 2026Yishu Zhang, Shushan Wu, Zhenzhong Zhang +8Batch Effect Correction Computational Pathology
University of North Carolina at Chapel Hill
scHelix: Asymmetric Dual-Stream Integration via Explicit Gene-Level Disentanglement
May 18, 2026Xichen Yan, Zelin Zang, Changxi Chi +8Batch Effect Correction Representation Disentanglement
Jinan University · Westlake University · HKISI +1
Stabilizing In-Context Multi-Source Domain Adaptation for Biomedical Images Through Controls
Apr 22, 2026Ana Sanchez-Fernandez, Thomas Pinetz, Werner Zellinger +1Multi-Source Domain Adaptation Batch Effect Correction
ELLIS Unit Linz, LIT AI Lab and Institute for Machine Learning, JKU Linz, Austria · Institute of Artificial Intelligence, Center for Medical Data Science, Medical University Vienna · Clinical Research Center for Medical AI, JKU Linz, Austria
INST-Align: Implicit Neural Alignment for Spatial Transcriptomics via Canonical Expression Fields
Apr 13, 2026Bonian Han, Cong Qi, Przemyslaw Musialski +1Batch Effect Correction Spatial Transcriptomics
New Jersey Institute of Technology, Newark, NJ, USA
Wasserstein Parallel Transport for Predicting the Dynamics of Statistical Systems
Mar 24, 2026Tristan Luca Saidi, Gonzalo Mena, Larry Wasserman +1Batch Effect Correction Causal Counterfactual Generation
Department of Statistics and Data Science, Carnegie Mellon University · Machine Learning Department, Carnegie Mellon University · Department of Economics, Emory University
Adversarial Batch Representation Augmentation for Batch Correction in High-Content Cellular Screening
Mar 5, 2026Lei Tong, Xujing Yao, Adam Corrigan +6Batch Effect Correction Domain Generalization
Data Sciences and Quantitative Biology, Discovery Sciences, AstraZeneca R&D, Cambridge, UK · School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK · College of Computer and Information Engineering, Nanjing Tech University, Nanjing, China +1
Batch effects can impair federated learning in multi-center omics studies
Dec 8, 2024Yuliya Burankova, Julian Klemm, Jens J. G. Lohmann +5Batch Effect Correction Secure Multi-Party Computation
Institute for Computational Systems Biomedicine, University of Hamburg, 22761 Hamburg, Germany · Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany · Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark +3

Oct 21, 2026J/K move · Enter open · S save

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

Sonja Topf, Ana Sanchez-Fernandez, Günter Klambauer

ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University, Linz, Austria · Clinical Research Institute for Medical AI, Johannes Kepler University, Linz, Austria

Distribution Shift Robustness Medical Image Benchmarks Domain Generalization Batch Effect Correction

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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.