A Dual Convolutional Neural Network Pipeline for Melanoma Diagnostics and Prognostics
This work addresses the need for more efficient melanoma diagnostics, which is crucial for patients and healthcare providers, but it is incremental as it builds on existing CNN methods.
The paper tackled melanoma diagnosis and prognosis by developing a pipeline with two convolutional neural networks, achieving an F1 score of 0.79 on data from the same distribution as training.
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.