An algorithm that can visually diagnose melanoma, the deadliest form of skin cancer. In particular, your algorithm will distinguish this malignant skin tumor from two types of benign lesions (nevi and seborrheic keratoses).
The data and objective are pulled from the 2017 ISIC Challenge on Skin Lesion Analysis Towards Melanoma Detection.
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Download and unzip the training data (5.3 GB).
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Download and unzip the validation data (824.5 MB).
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Download and unzip the test data (5.1 GB).
Ability of CNN to distinguish between malignant melanoma and the benign skin lesions (nevus, seborrheic keratosis) by calculating the area under the receiver operating characteristic curve (ROC AUC) corresponding to this binary classification task.
The top scores (from the ISIC competition) in this category can be found in the image below.
All of the skin lesions examined are caused by abnormal growth of either melanocytes or keratinocytes, which are two different types of epidermal skin cells. Melanomas and nevi are derived from melanocytes, whereas seborrheic keratoses are derived from keratinocytes.
Tests the ability of your CNN to distinuish between melanocytic and keratinocytic skin lesions by calculating the area under the receiver operating characteristic curve (ROC AUC) corresponding to this binary classification task.
The top scores in this category (from the ISIC competition) can be found in the image below.
Average of the ROC AUC values from the first two categories.
The top scores in this category (from the ISIC competition) can be found in the image below.
