Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning

TL;DR

This paper presents a novel data synthesis method combined with deep learning for improved detection and tracking of skin cancer, demonstrating superior performance over traditional methods and human comparison.

Contribution

The work introduces a new data synthesis technique for skin lesion images and a CNN-based system trained on this data for enhanced detection and tracking.

Findings

Synthetic data improves detection accuracy

System outperforms traditional detection methods

Comparable to trained human experts

Abstract

Dense object detection and temporal tracking are needed across applications domains ranging from people-tracking to analysis of satellite imagery over time. The detection and tracking of malignant skin cancers and benign moles poses a particularly challenging problem due to the general uniformity of large skin patches, the fact that skin lesions vary little in their appearance, and the relatively small amount of data available. Here we introduce a novel data synthesis technique that merges images of individual skin lesions with full-body images and heavily augments them to generate significant amounts of data. We build a convolutional neural network (CNN) based system, trained on this synthetic data, and demonstrate superior performance to traditional detection and tracking techniques. Additionally, we compare our system to humans trained with simple criteria. Our system is intended for potential clinical use to augment the capabilities of healthcare providers. While domain-specific, we believe the methods invoked in this work will be useful in applying CNNs across domains that suffer from limited data availability.