Fine-tuning of explainable CNNs for skin lesion classification based on dermatologists' feedback towards increasing trust
This work addresses trust issues for users of medical AI systems, specifically in dermatology, and is incremental as it builds on existing CNN and Grad-CAM techniques.
The paper tackles the problem of increasing user trust in CNN-based skin lesion classifiers by proposing a fine-tuning method that incorporates simultaneous user feedback on both classification and visual explanations, resulting in improved visual explanations while preserving classification accuracy.
In this paper, we propose a CNN fine-tuning method which enables users to give simultaneous feedback on two outputs: the classification itself and the visual explanation for the classification. We present the effect of this feedback strategy in a skin lesion classification task and measure how CNNs react to the two types of user feedback. To implement this approach, we propose a novel CNN architecture that integrates the Grad-CAM technique for explaining the model's decision in the training loop. Using simulated user feedback, we found that fine-tuning our model on both classification and explanation improves visual explanation while preserving classification accuracy, thus potentially increasing the trust of users in using CNN-based skin lesion classifiers.