Detection and Localization of Melanoma Skin Cancer in Histopathological Whole Slide Images

TL;DR

This paper introduces a deep learning approach using a single CNN to detect and localize melanoma in histopathological whole slide images, achieving high accuracy and aiding pathologists.

Contribution

It presents a novel CNN-based method that simultaneously detects and localizes melanoma in WSIs with high precision, streamlining diagnosis.

Findings

F1 score of 0.992 on patch classification

Sensitivity of 0.99 on unseen data

Effective localization of melanoma regions

Abstract

Melanoma diagnosed and treated in its early stages can increase the survival rate. A projected increase in skin cancer incidents and a dearth of dermatopathologists have emphasized the need for computational pathology (CPATH) systems. CPATH systems with deep learning (DL) models have the potential to identify the presence of melanoma by exploiting underlying morphological and cellular features. This paper proposes a DL method to detect melanoma and distinguish between normal skin and benign/malignant melanocytic lesions in Whole Slide Images (WSI). Our method detects lesions with high accuracy and localizes them on a WSI to identify potential regions of interest for pathologists. Interestingly, our DL method relies on using a single CNN network to create localization maps first and use them to perform slide-level predictions to determine patients who have melanoma. Our best model provides favorable patch-wise classification results with a 0.992 F1 score and 0.99 sensitivity on unseen data. The source code is https://github.com/RogerAmundsen/Melanoma-Diagnosis-and-Localization-from-Whole-Slide-Images-using-Convolutional-Neural-Networks.