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In this paper, we propose a CNN fine-tuning method which enables users togive simultaneous feedback on two outputs: the classification itself and thevisual explanation for the classification. We present the effect of thisfeedback strategy in a skin lesion classification task and measure how CNNsreact to the two types of user feedback. To implement this approach, we proposea novel CNN architecture that integrates the Grad-CAM technique for explainingthe model's decision in the training loop. Using simulated user feedback, wefound that fine-tuning our model on both classification and explanationimproves visual explanation while preserving classification accuracy, thuspotentially increasing the trust of users in using CNN-based skin lesionclassifiers.

Fine-tuning of explainable CNNs for skin lesion classification based on dermatologists' feedback towards increasing trust