TorchXRayVision: A library for chest X-ray datasets and models. Including pre-trained models.
Motivation: While there are many publications focusing on the prediction of radiological and clinical findings from chest X-ray images much of this work is inaccessible to other researchers.
- In the case of researchers addressing clinical questions it is a waste of time for them to train models from scratch. To address this, TorchXRayVision provides pre-trained models which are trained on large cohorts of data and enables 1) rapid analysis of large datasets 2) feature reuse for few-shot learning.
- In the case of researchers developing algorithms it is import to robustly evaluate models using multiple external datasets. Metadata associated with each dataset can vary greatly which makes it difficult to apply methods to multiple datasets. TorchXRayVision provides access to many datasets in a uniform way so that they can be swapped out with a single line of code. These datasets can also be merged and filtered to construct specific distributional shifts for studying generalization.
https://github.com/mlmed/torchxrayvision
Joseph Paul Cohen, Joseph Viviano, Paul Morrison, Rupert Brooks, Mohammad Hashir, Hadrien Bertrand
TorchXRayVision: A library of chest X-ray datasets and models.
https://github.com/mlmed/torchxrayvision, 2020
@article{Cohen2020xrv,
author = {Cohen, Joseph Paul and Viviano, Joseph and Morrison, Paul and Brooks, Rupert and Hashir, Mohammad and Bertrand, Hadrien},
journal = {https://github.com/mlmed/torchxrayvision},
title = {{TorchXRayVision: A library of chest X-ray datasets and models}},
url = {https://github.com/mlmed/torchxrayvision},
year = {2020}
}