Closing the Domain Gap in Biomedical Imaging by In-Context Control Samples
Paper • 2604.20824 • Published
Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Server error while post-processing the rows. It seems the image can't be loaded with PIL.Image and could be corrupted.
Error code: TransformRowsProcessingError
Exception: UnidentifiedImageError
Message: cannot identify image file <_io.BytesIO object at 0x7f527d5c7970>
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/rows_utils.py", line 37, in _transform_row
transformed_row[featureName] = get_cell_value(
~~~~~~~~~~~~~~^
dataset=dataset,
^^^^^^^^^^^^^^^^
...<9 lines>...
hf_token=hf_token,
^^^^^^^^^^^^^^^^^^
)
^
File "/src/libs/libcommon/src/libcommon/viewer_utils/features.py", line 579, in get_cell_value
return image(
dataset=dataset,
...<9 lines>...
json_path=json_path,
)
File "/src/libs/libcommon/src/libcommon/viewer_utils/features.py", line 122, in image
value = PILImage.open(BytesIO(value["bytes"]))
File "/usr/local/lib/python3.14/site-packages/PIL/Image.py", line 3715, in open
raise UnidentifiedImageError(msg)
PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7f527d5c7970>Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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.
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