| --- |
| 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) |
|
|
|
|
