SIIM-Melanoma-Classification
This repository is a summary of my work building models to classify melanomas as malignant or benign based on ISIC's melanoma archive.
Skin cancer is the most prevalent type of cancer. Melanoma, specifically, is responsible for 75% of skin cancer deaths, despite being the least common skin cancer. The American Cancer Society estimates over 100,000 new melanoma cases will be diagnosed in 2020. It's also expected that almost 7,000 people will die from the disease. As with other cancers, early and accurate detection—potentially aided by data science—can make treatment more effective.
.
Currently, dermatologists evaluate every one of a patient's moles to identify outlier lesions or “ugly ducklings” that are most likely to be melanoma. Existing AI approaches have not adequately considered this clinical frame of reference. Dermatologists could enhance their diagnostic accuracy if detection algorithms take into account “contextual” images within the same patient to determine which images represent a melanoma. If successful, classifiers would be more accurate and could better support dermatological clinic work.
Rank: 1154/3319 Top AUC-ROC: 0.9411
Teammates: Jack Zhang, Alice Liu, Kevin Tong.
Applied to a camera app here: https://github.com/michael1ding/Melanoma-Camera-Classification