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Biomedical Vision Gets a Batch-Shift Scoreboard
BatchBench tests whether image models survive unseen experimental batches in the large JUMP Cell Painting collection.
Summary
BatchBench tests whether image models survive unseen experimental batches in the large JUMP Cell Painting collection.
Biomedical images can change with laboratory, plate, instrument and acquisition conditions even when the biological question stays the same. BatchBench turns those shifts into a public robustness leaderboard built on JUMP Cell Painting. The benchmark separates familiar from unseen experimental batches and data sources so gains cannot be mistaken for ordinary in-distribution accuracy. It supplies evaluation infrastructure rather than a clinical model, and performance on Cell Painting does not establish diagnostic reliability.
Why it matters
BatchBench tests whether image models survive unseen experimental batches in the large JUMP Cell Painting collection.
Limits and context
- Biomedical images can change with laboratory, plate, instrument and acquisition conditions even when the biological question stays the same.
- The benchmark separates familiar from unseen experimental batches and data sources so gains cannot be mistaken for ordinary in-distribution accuracy.
- It supplies evaluation infrastructure rather than a clinical model, and performance on Cell Painting does not establish diagnostic reliability.
Key claims
BatchBench tests whether image models survive unseen experimental batches in the large JUMP Cell Painting collection.
Qualification: Biomedical images can change with laboratory, plate, instrument and acquisition conditions even when the biological question stays the same.
Evidence: source-2026-10-22-008
Sources
- arXiv preprint 2610.30104, original v1arXiv · primary research
Corrections
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