TopoResNet: A Hybrid Deep Learning Architecture and Its Application to Skin Lesion Classification
Chuan-Shen Hu, Austin Lawson, Jung‐Sheng Chen, Yu-Min Chung, Clifford Smyth, Shih-Min Yang
Abstract
The application of artificial intelligence (AI) to various medical subfields has been a popular topic of research in recent years. In particular, deep learning has been widely used and has proven effective in many cases. Topological data analysis (TDA)—a rising field at the intersection of mathematics, statistics, and computer science—offers new insights into data. In this work, we develop a novel deep learning architecture that we call TopoResNet that integrates topological information into the residual neural network architecture. To demonstrate TopoResNet, we apply it to a skin lesion classification problem. We find that TopoResNet improves the accuracy and the stability of the training process.