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 and Shih-Min Yang
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Chuan-Shen Hu: Department of Mathematics, National Taiwan Normal University, Taipei City 11365, Taiwan
Austin Lawson: Department of Mathematics, University of Tennessee Knoxville, Knoxville, TN 37916, USA
Jung-Sheng Chen: Department of Medical Research, E-Da Hospital, Kaohsiung City 824410, Taiwan
Yu-Min Chung: Eli Lilly and Company, Indianapolis, IN 46225, USA
Clifford Smyth: Department of Mathematics and Statistics, University of North Carolina at Greensboro, Greensboro, NC 27412, USA
Shih-Min Yang: Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei City 11365, Taiwan

Mathematics, 2021, vol. 9, issue 22, 1-22

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.

Keywords: deep learning; topological data analysis; persistent homology; persistence statistics; persistence curves; hybrid models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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