  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%. Furthermore, we implement a semi-supervised method using knowledge
distillation and find that though it is outperformed by CheXseg, it exceeds the
performance (mIoU) of the best fully-supervised method by 4.83%. Our best
method is able to match radiologist agreement on three out of ten pathologies
and reduces the overall performance gap by 71.6% as compared to
weakly-supervised methods.
