Datasets:
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license: bsd-3-clause
pretty_name: JUMPCP subset for CS-ARM-BN
task_categories:
- image-classification
language:
- en
tags:
- cell-painting
- jump-cp
- microscopy
- biological-imaging
- image-analysis
- computer-vision
- plate-effects
---
<p align="center">
<a href="https://arxiv.org/abs/2604.20824">
<img src="https://img.shields.io/badge/arXiv-2604.20824-b31b1b.svg" alt="arXiv">
</a>
<a href="https://github.com/ml-jku/cs-arm-bn">
<img src="https://img.shields.io/badge/Github-181717?logo=github&logoColor=white" alt="GitHub">
</a>
</p>
<p align="center">
<img src="figures/data_overview.png" alt="Dataset overview" width="750"/>
</p>
<p align="center">
</p>
# Multi-Source Domain Adaptation for Bioimaging Data (MSCDA-BioIm)
**MSCDA-BioIm** is a biomedical microscopy benchmark for evaluating test-time and in-context domain adaptation under realistic batch effects. Built from the large-scale JUMP-CP dataset, it targets mechanism-of-action (MoA) classification using five-channel images of compounds associated with eight well-defined MoA classes. The dataset is organized by experimental batches and imaging sources, enabling controlled evaluation of generalization to unseen batches, cross-source transfer, small target context sizes, and label-shifted target batches.
A key feature of **MSCDA-BioIm** is the inclusion of negative control samples in every experimental batch. These unperturbed samples provide a stable reference for estimating batch-specific technical variation, making the dataset especially suitable for studying control-aware adaptation methods such as CS-ARM-BN.
For more information, please see our [paper](https://arxiv.org/abs/2604.20824) and [code](https://github.com/ml-jku/cs-arm-bn)
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