  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.
