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

Paper recorded by Signals 4 on 2026-10-21 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.AI · 人工智能 · first seen 2026-10-22

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 BatchBe

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#208 most recent of 960 cs.AI papers we have recorded · ↑ newer: Asymmetric On-Policy Distillation Learns In-Context Exploration · ↓ older: Differentially Private Sparse Reward Estimation with Preference Feedba
Cite this page: BatchBench: Benchmarking AI Robustness to Batch Effects and Domain Shift in Biomedical Images - A JUMP Cell Painting Leaderboard: the #208 most recent of 960 cs.AI papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/batchbench-benchmarking-ai-robustness-to-batch-effects-and-domain-shift-in-biome.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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