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Found 5 papers, 2 papers with code
GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Texts
Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data.
GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions
The effectiveness of the approach was assessed by evaluating its performance in removing identifiable facial attributes to increase the anonymity of the given individual face.
Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles
Because of the overall scarcity of patients with ultra-rare disorders, it is infeasible to directly train a model on them.
Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders
Furthermore, we created simple baselines of few-shot meta-learning methods to improve our base feature descriptor.
DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning
Facial analysis technologies have recently measured up to the capabilities of expert clinicians in syndrome identification.
