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

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Authors Jeremy Kawahara, S. Daneshvar, G. Argenziano, G. Hamarneh
Journal/Conference Name IEEE Journal of Biomedical and Health Informatics
Paper Category
Paper Abstract We propose a multitask deep convolutional neural network, trained on multimodal data (clinical and dermoscopic images, and patient metadata), to classify the 7-point melanoma checklist criteria and perform skin lesion diagnosis. Our neural network is trained using several multitask 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 multimodal 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.
Date of publication 2019
Code Programming Language Jupyter Notebook
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