Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training

12/02/2020 ∙ by Andrei Margeloiu, et al. ∙ 20

We investigate the influence of adversarial training on the interpretability of convolutional neural networks (CNNs), specifically applied to diagnosing skin cancer. We show that gradient-based saliency maps of adversarially trained CNNs are significantly sharper and more visually coherent than those of standardly trained CNNs. Furthermore, we show that adversarially trained networks highlight regions with significant color variation within the lesion, a common characteristic of melanoma. We find that fine-tuning a robust network with a small learning rate further improves saliency maps' sharpness. Lastly, we provide preliminary work suggesting that robustifying the first layers to extract robust low-level features leads to visually coherent explanations.



There are no comments yet.


page 1

page 2

page 3

page 4

page 5

page 7

page 11

page 12

Code Repositories


Code for the NeurIPS 2020 Workshop paper "Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training"

view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.