Skin cancer detection based on deep learning and entropy to detect outlier samples

TL;DR

This paper presents deep learning methods, including ensemble CNNs and entropy-based outlier detection, to improve skin cancer diagnosis from images and meta-data, achieving top placements in the ISIC 2019 challenge.

Contribution

The authors introduce novel approaches for handling outlier classes and integrating meta-data with CNNs in skin cancer detection tasks.

Findings

Achieved 3rd and 4th places in ISIC 2019 challenge

Effective outlier detection method using entropy

Successful integration of meta-data with image analysis

Abstract

We describe our methods that achieved the 3rd and 4th places in tasks 1 and 2, respectively, at ISIC challenge 2019. The goal of this challenge is to provide the diagnostic for skin cancer using images and meta-data. There are nine classes in the dataset, nonetheless, one of them is an outlier and is not present on it. To tackle the challenge, we apply an ensemble of classifiers, which has 13 convolutional neural networks (CNN), we develop two approaches to handle the outlier class and we propose a straightforward method to use the meta-data along with the images. Throughout this report, we detail each methodology and parameters to make it easy to replicate our work. The results obtained are in accordance with the previous challenges and the approaches to detect the outlier class and to address the meta-data seem to be work properly.