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.
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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x-ray
X-ray
R
13.4
9.7
R
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\%.
73.1\%.
R
71.6
57.2