CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation
arXiv:2102.10484
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.
arXiv commentAccepted to Medical Imaging with Deep Learning (MIDL) Conference 2021
References in corpus (7)
- Distilling the Knowledge in a Neural Network
- Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
- DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
- Deep neural network or dermatologist?
- MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models
- Learning to segment images with classification labels
- Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging