A Dual Convolutional Neural Network Pipeline for Melanoma Diagnostics
  and Prognostics
Marie Bø-Sande Edvin Benjaminsen Neel Kanwal Saul Fuster Helga Hardardottir Ingrid Lundal Emiel A. M. Janssen Kjersti Engan
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.