Proof-of-principle neural network models for classification, attribution, creation, style-mixing, and morphing of image data for genetic conditions

Neural networks have shown strong potential to aid the practice of healthcare. Mainly due to the need for large datasets, these applications have focused on common medical conditions, where much more data is typically available. Leveraging publicly available data, we trained a neural network classifier on images of rare genetic conditions with skin findings. We used approximately100 images per condition to classify 6 different genetic conditions. Unlike other work related to these types of images, we analyzed both preprocessed images that were cropped to show only the skin lesions, as well as more complex images showing features such as the entire body segment, patient, and/or the background. The classifier construction process included attribution methods to visualize which pixels were most important for computer-based classification. Our classifier was significantly more accurate than pediatricians or medical geneticists for both types of images. Next, we trained two generative adversarial networks to generate new images. The first involved all of the genetic conditions and was used for style-mixing to demonstrate how the diversity of small datasets can be increased. The second focused on different disease stages for one condition and depicted how morphing can illustrate the disease progression of this condition. Overall, our findings show how computational techniques can be applied in multiple ways to small datasets to enhance the study of rare genetic diseases.