A Deep Learning-Based Melanocytic Nevi Classification Algorithm by Leveraging Physiologic-Inspired Knowledge and Channel Encoded Information

Melanocytic nevi (MN), which are the most prevalent benign skin tumors, can be classified into three subtypes based on the depth of the nevus cell nests: junctional, compound, and intradermal nevi. Among these subtypes, intradermal nevi pose a lower risk of malignancy, whereas junctional nevi carry a higher risk of malignancy. To facilitate early stage diagnosis, mitigate unnecessary biopsies, and reduce medical costs, this paper presents the first study on the classification of benign melanocytic nevi. We propose an algorithm that utilizes depth-relevant, channel-specific, and channel-shared information from dermoscopy images. This approach draws inspiration from the physical principles underlying dermoscopy imaging and the physiological essence of nevi. We employed CNN and transformer blocks to capture local and long-range contextual information. In addition, our physiology-inspired module focuses on learning depth-related features from the color channels. To assess the performance of our proposed method, we curated a dataset of dermoscopic images of MN and compared it with two human experts as well as classic CNN and Transformer baseline models. Our results showed that the proposed automated method achieved an average accuracy of 70.23%, surpassing the best-performing baseline by 3.6%, and outperforming human experts by nearly 20%. Furthermore, we conducted an ablation study to confirm the effectiveness of our algorithm and to analyze its clinical significance.