Development Of Deep Learning in Diagnosing Pathology

Abstract

Skin cancer is one of the most common types of cancer worldwide, with a particularly high incidence in Indonesia. According to Globocan 2020 data, there were approximately 18,000 cases of skin cancer with nearly 3,000 deaths, with melanoma having the highest mortality rate. A study at Dr. Cipto Mangunkusumo General Hospital (2014–2017) showed that malignant melanoma accounted for 5.7% of all skin cancer cases, with the majority of patients presenting at an advanced stage. Pathological diagnosis remains the gold standard for confirming melanocyte lesions, but it is subjective with variability reaching 45.5%.The development of Whole Slide Imaging (WSI) and Deep Learning (DL) has enabled the implementation of more accurate and consistent computer-aided pathological diagnosis systems. Several previous studies, such as those by Hekler et al., Brinker et al., and Li et al., have demonstrated that Convolutional Neural Network (CNN)-based models can match or exceed the performance of human pathologists. However, two major challenges remain: the model's limitations in distinguishing atypical melanocytic lesions and decreased performance due to staining variations across medical centers. This study aims to develop a DL-based intelligent pathological diagnosis model using WSI images that can accurately distinguish benign, atypical, and malignant melanocytic lesions and is robust to staining variations, to improve the effectiveness of skin cancer diagnosis in Indonesia.