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

arXiv: https://arxiv.org/abs/2304.01399 · HTML: https://mlsanity.com/paper/2304.01399/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience](https://mlsanity.com/paper/2001.09219/) (2020)
- [A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop](https://mlsanity.com/paper/1708.01104/) (2017)
- [The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. Technical Report](https://mlsanity.com/paper/2005.09448/) (2020)

## Abstract (arXiv)

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.