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The 2021 Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA) dataset pairs raw quantitative MRI (qMRI) data, image data, and dense labels of tissues and pathology for end-to-end exploration and evaluation of the MR imaging pipeline.
This repository contains the building blocks for training and benchmarking models with the SKM-TEA dataset, such as PyTorch dataloaders, evaluation metrics, an 8233 d baselines. It also contains tutorials for using the dataset and codebase. It utilizes Meddlr and PyTorch Lightning for training, evaluation, and machine utilities.
pip install skm-teaInstalling locally: For local development, fork and clone the repo and run
pip install -e ".[dev]"
_Installing from main: For most up-to-date code without a local install, run
pip install "skm-tea @ git+https://github.com/StanfordMIMI/skm-tea@main"
Configure your paths and get going!
import meddlr as mr
import os
# (Optional) Configure and save machine/cluster preferences.
# This only has to be done once and will persist across sessions.
cluster = mr.Cluster()
cluster.set(results_dir="/path/to/save/results", data_dir="/path/to/datasets")
cluster.save()
# OR set these as environment variables.
os.environ["MEDDLR_RESULTS_DIR"] = "/path/to/save/results"
os.environ["MEDDLR_DATASETS_DIR"] = "/path/to/datasets"
# TODO: Add how to easily fetch dataset.Documentation for downloading and using the SKM-TEA dataset can be found in DATASET.md. Benchmarks are constantly evolving - check this repository for up-to-date baselines.
A list of pre-trained models can be found here and in Google Drive.
To use them, pass the google drive urls for the config and weights (model) files to st.build_deployment_model:
import skm_tea as st
# Make sure to add "download://" before the url!
model = st.get_model_from_zoo(
cfg_or_file="download://https://drive.google.com/file/d/1DTSfmaGu2X9CpE5qW52ux63QrIs9L0oa/view?usp=sharing",
weights_path="download://https://drive.google.com/file/d/1no9-COhdT2Ai3yuxXpSYMpE76hbqZTWn/view?usp=sharing",
)
This repository is being developed at the Stanford's MIMI Lab. Please reach out to arjundd [at] stanford [dot] edu if you would like to use or contribute to SKM-TEA.
If you use the SKM-TEA dataset or code, please use the following BibTex:
@inproceedings{desai2021skm,
title={SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation},
author={Desai, Arjun D and Schmidt, Andrew M and Rubin, Elka B and Sandino, Christopher Michael and Black, Marianne Susan and Mazzoli, Valentina and Stevens, Kathryn J and Boutin, Robert and Re, Christopher and Gold, Garry E and others},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021}
}