Keywords: Semi-Supervised Segmentation, Saliency Maps, Localization Performance
TL;DR: We develop CheXseg, a semi-supervised method for multi-pathology segmentation that leverages both the pixel-level expert annotations and the saliency maps generated by image classification models.
Abstract: Medical image segmentation models are typically supervised by expert annotations at the pixel-level, which can be expensive to acquire. In this work, we propose a method that combines the high quality of pixel-level expert annotations with the scale of coarse DNN-generated saliency maps for training multi-label semantic segmentation models. We demonstrate the application of our semi-supervised method, which we call CheXseg, on multi-label chest X-ray interpretation. We find that CheXseg improves upon the performance (mIoU) of fully-supervised methods that use only pixel-level expert annotations by 9.7% and weakly-supervised methods that use only DNN-generated saliency maps by 73.1%. Our best method is able to match radiologist agreement on three out of ten pathologies and reduces the overall performance gap by 57.2% as compared to weakly-supervised methods.
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Paper Type: both
Primary Subject Area: Segmentation
Secondary Subject Area: Application: Radiology
Source Code Url: https://github.com/stanfordmlgroup/CheXseg
Data Set Url: https://stanfordmlgroup.github.io/competitions/chexpert/
Source Latex: zip