# Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification

arXiv: https://arxiv.org/abs/1809.03851 · HTML: https://mlsanity.com/paper/1809.03851/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 9 of 9)

- [ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition](https://mlsanity.com/paper/2208.10431/) (2022)
- [A survey, review, and future trends of skin lesion segmentation and classification](https://mlsanity.com/paper/2208.12232/) (2022)
- [Empowering CAM-Based Methods with Capability to Generate Fine-Grained and High-Faithfulness Explanations](https://mlsanity.com/paper/2303.09171/) (2023)
- [Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis](https://mlsanity.com/paper/2103.05112/) (2021)
- [Leveraging Global Binary Masks for Structure Segmentation in Medical Images](https://mlsanity.com/paper/2205.09107/) (2022)
- [Parameter-Efficient Transformer with Hybrid Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2211.09533/) (2022)
- [Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning](https://mlsanity.com/paper/2305.20058/) (2023)
- [Targeted Data Augmentation for bias mitigation](https://mlsanity.com/paper/2308.11386/) (2023)
- [A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifiers](https://mlsanity.com/paper/2201.02771/) (2022)

## Abstract (arXiv)

Because of their state-of-the-art performance in computer vision, CNNs are becoming increasingly popular in a variety of fields, including medicine. However, as neural networks are black box function approximators, it is difficult, if not impossible, for a medical expert to reason about their output. This could potentially result in the expert distrusting the network when he or she does not agree with its output. In such a case, explaining why the CNN makes a certain decision becomes valuable information. In this paper, we try to open the black box of the CNN by inspecting and visualizing the learned feature maps, in the field of dermatology. We show that, to some extent, CNNs focus on features similar to those used by dermatologists to make a diagnosis. However, more research is required for fully explaining their output.