Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets

Abstract

We propose a multi-task deep convolutional neural network, trained on multi-modal data (clinical and dermoscopic images, and patient meta-data), to classify the 7-point melanoma checklist criteria and perform skin lesion diagnosis. Our neural network is trained using several multi-task loss functions, where each loss considers different combinations of the input modalities, which allows our model to be robust to missing data at inference time. Our final model classifies the 7-point checklist and skin condition diagnosis, produces multi-modal feature vectors suitable for image retrieval, and localizes clinically discriminant regions. We benchmark our approach using 1011 lesion cases, and report comprehensive results over all 7-point criteria and diagnosis. We also make our dataset (images and metadata) publicly available online at http://derm.cs.sfu.ca.

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