  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.
