CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation
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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 13.4 and weakly-supervised methods that use only DNN-generated saliency maps by 91.2 distillation and find that though it is outperformed by CheXseg, it exceeds the performance (mIoU) of the best fully-supervised method by 4.83 method is able to match radiologist agreement on three out of ten pathologies and reduces the overall performance gap by 71.6 weakly-supervised methods.
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