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

Abstract

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 approximately 100 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.

Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

“…For dermatology, this means using neural networks pre‐trained on vast datasets (not necessarily medical) and fine‐tuning them for specific tasks like diagnosing hair and scalp disorders 13 . This approach is particularly advantageous in situations where medical data is scarce or when rare conditions are involved 14 . Transfer learning can mitigate the data scarcity issue also by leveraging pre‐existing models, thus enhancing the precision of diagnoses.…”

“… 13 This approach is particularly advantageous in situations where medical data is scarce or when rare conditions are involved. 14 Transfer learning can mitigate the data scarcity issue also by leveraging pre‐existing models, thus enhancing the precision of diagnoses. The application of deep neural transfer learning in diagnosing hair and scalp disorders could revolutionize dermatological diagnostics.…”