A Dual Convolutional Neural Network Pipeline for Melanoma Diagnostics and Prognostics

Marie Bø-Sande, Edvin Benjaminsen, Neel Kanwal, SAUL FUSTER, Helga Hardardottir, Ingrid Lundal, Emilius A.M. Janssen, Kjersti Engan

Keywords: Segmentation, Automated Diagnosis, Classification, Melanoma, Skin Cancer, Prognosis

TL;DR: Authors propose a DL pipeline which may serve as a valuable instrument in aiding pathologists for comprehensive patient diagnoses and prognoses in an end-to-end fashion.

Abstract: Melanoma is a type of cancer that begins in the cells controlling the pigment of the skin, and it is often referred to as the most dangerous skin cancer. Diagnosing melanoma can be time-consuming, and a recent increase in melanoma incidents indicates a growing demand for a more efficient diagnostic process. This paper presents a pipeline for melanoma diagnostics, leveraging two convolutional neural networks, a diagnosis, and a prognosis model. The diagnostic model is responsible for localizing malignant patches across whole slide images and delivering a patient-level diagnosis as malignant or benign. Further, the prognosis model utilizes the diagnostic model's output to provide a patient-level prognosis as good or bad. The full pipeline has an F1 score of 0.79 when tested on data from the same distribution as it was trained on.

Submission Number: 15