Abstract: Interpretability of deep neural networks in medical imaging is becoming an important technique to understand network classification decisions and increase doctors' trust. Available methods for visual interpretation, though, tend to highlight only the most discriminant areas, which is suboptimal for clinical output. We propose a novel deep visualization framework for improving weakly-supervised lesion localization. The framework applies an iterative approach where, in each step, the interpretation maps focus on different, less discriminative areas of the images, but still important for the final classification, reaching a more refined localization of abnormalities. We evaluate the performance of the method for the localization of diabetic retinopathy lesions in color fundus images. The results show the obtained visualization maps are able to detect more lesions after the iterative procedure in the case of more severely affected retinas.
Keywords: Deep learning, interpretability, saliency, visualization, inpainting
Author Affiliation: RadboudUMC Nijmegen