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Medical Segmentation Decathlon Dataset for Machine Learning

Install DagsHub:

pip install dagshub
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To stream this data directly on DagsHub

from dagshub.streaming import DagsHubFilesystem

fs = DagsHubFilesystem(".", repo_url="https://dagshub.com/DagsHub-Datasets/msd-dataset")

fs.listdir("s3://msd-for-monai")
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Description

With recent advances in machine learning, semantic segmentation algorithms are becoming increasingly general purpose and translatable to unseen tasks. Many key algorithmic advances in the field of medical imaging are commonly validated on a small number of tasks, limiting our understanding of the generalisability of the proposed contributions. A model which works out-of-the-box on many tasks, in the spirit of AutoML, would have a tremendous impact on healthcare. The field of medical imaging is also missing a fully open source and comprehensive benchmark for general purpose algorithmic validation and testing covering a large span of challenges, such as: small data, unbalanced labels, large-ranging object scales, multi-class labels, and multimodal imaging, etc. This challenge and dataset aims to provide such resource through the open sourcing of large medical imaging datasets on several highly different tasks, and by standardising the analysis and validation process.

Additional information

Update frequency

This is a static dataset; however, tutorials and resources will be updated as they are developed.

Related datasets

Allen Brain Observatory – Visual Coding AWS Public Data Set

Allen Cell Imaging Collections

Biological and Physical Sciences (BPS) Microscopy Benchmark Training Dataset

Cancer Cell Line Encyclopedia (CCLE)

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