Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Results

TL;DR

This paper reviews and improves automated melanoma screening methods, demonstrating that modern computer vision models significantly outperform traditional approaches and emphasizing the need for reproducibility in research.

Contribution

It reimplements baseline techniques, introduces two novel models, and advocates for better reproducibility in automated melanoma diagnosis research.

Findings

Deep neural network model achieves 89.3% AUC

Streamlined pipelines outperform traditional models

Reproducibility issues are addressed and discussed

Abstract

In this paper we survey, analyze and criticize current art on automated melanoma screening, reimplementing a baseline technique, and proposing two novel ones. Melanoma, although highly curable when detected early, ends as one of the most dangerous types of cancer, due to delayed diagnosis and treatment. Its incidence is soaring, much faster than the number of trained professionals able to diagnose it. Automated screening appears as an alternative to make the most of those professionals, focusing their time on the patients at risk while safely discharging the other patients. However, the potential of automated melanoma diagnosis is currently unfulfilled, due to the emphasis of current literature on outdated computer vision models. Even more problematic is the irreproducibility of current art. We show how streamlined pipelines based upon current Computer Vision outperform conventional models - a model based on an advanced bags of words reaches an AUC of 84.6%, and a model based on deep neural networks reaches 89.3%, while the baseline (a classical bag of words) stays at 81.2%. We also initiate a dialog to improve reproducibility in our community