The links below are generally up to date, but check out our GitHub and Hugging Face pages to be abreast of our newest repositories and demos.

SKM-TEA: Stanford Knee MRI with Multi-Task Evaluation >>

SKM-TEA is a collection of quantitative knee MRI (qMRI) scans that enables end-to-end, clinically-relevant evaluation of MRI reconstruction and analysis tools. This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four tissues, and bounding box annotations for sixteen clinically relevant pathologies. We provide a framework for using qMRI parameter maps, along with image reconstructions and dense image labels, for measuring the quality of qMRI biomarker estimates extracted from MRI reconstruction, segmentation, and detection techniques. Finally, we use this framework to benchmark state-of-the-art baselines on this dataset. Dataset access, code, and benchmarks are available in the SKM-TEA GitHub repo. The peer-reviewed manuscript presented at the Thirty-Fifth Annual Conference on Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks track is available here.

DOSMA: Deep Open-Source Medical Image Analysis >>

DOSMA is an AI-powered Python library for medical image analysis. This includes, but is not limited to:

  • image processing (denoising, super-resolution, registration, segmentation, etc.)
  • quantitative fitting and image analysis
  • anatomical visualization and analysis (patellar tilt, femoral cartilage thickness, etc.)

    • We hope that this open-source pipeline will be useful for quick anatomy/pathology analysis and will serve as a hub for adding support for analyzing different anatomies and scan sequences. The GitHub repo is available here.

    MEDDLR: A Config-Driven ML Framework Built to Simplify Medical Image Reconstruction and Analysis Problems >>

    TBD

    TorchXRayVision >>

    TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models. It provides a common interface and common pre-processing chain for a wide set of publicly available chest X-ray datasets. In addition, a number of classification and representation learning models with different architectures, trained on different data combinations, are available through the library to serve as baselines or feature extractors. The GitHub repo is available here and the publication pre-print is available here.