Advancing Melanoma Diagnosis with Self-Supervised Neural Networks: Evaluating the Effectiveness of Different Techniques

TL;DR

This study explores various self-supervised learning techniques to improve deep learning models for melanoma classification, demonstrating notable performance gains especially with corruption removal, and highlights future directions for enhancing model accuracy.

Contribution

The paper evaluates multiple self-supervision methods for melanoma detection, identifying corruption removal as particularly effective and suggesting avenues for further improvement with advanced techniques.

Findings

Self-supervision improves melanoma classification accuracy.

Corruption removal method shows significant performance boost.

Potential for further gains with extended training and new methods.

Abstract

We investigate the potential of self-supervision in improving the accuracy of deep learning models trained to classify melanoma patches. Various self-supervision techniques such as rotation prediction, missing patch prediction, and corruption removal were implemented and assessed for their impact on the convolutional neural network's performance. Preliminary results suggest a positive influence of self-supervision methods on the model's accuracy. The study notably demonstrates the efficacy of the corruption removal method in enhancing model performance. Despite observable improvements, we conclude that the self-supervised models have considerable potential for further enhancement, achievable through training over more epochs or expanding the dataset. We suggest exploring other self-supervision methods like Bootstrap Your Own Latent (BYOL) and contrastive learning in future research, emphasizing the cost-benefit trade-off due to their resource-intensive nature. The findings underline the promise of self-supervision in augmenting melanoma detection capabilities of deep learning models.