Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles

• URL: http://arxiv.org/abs/2211.06764v1
• Date: Sat, 12 Nov 2022 23:28:54 GMT
• Title: Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles
• Authors: Alexander Hustinx, Fabio Hellmann, \"Omer S\"umer, Behnam Javanmardi, Elisabeth Andr\'e, Peter Krawitz, Tzung-Chien Hsieh
• Abstract summary: We analyze the influence of replacing a DCNN with a state-of-the-art face recognition approach, iResNet with ArcFace. Our proposed ensemble model achieves state-of-the-art performance on both seen and unseen disorders.
• Score: 52.77024349608834
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Rare genetic disorders affect more than 6% of the global population. Reaching a diagnosis is challenging because rare disorders are very diverse. Many disorders have recognizable facial features that are hints for clinicians to diagnose patients. Previous work, such as GestaltMatcher, utilized representation vectors produced by a DCNN similar to AlexNet to match patients in high-dimensional feature space to support "unseen" ultra-rare disorders. However, the architecture and dataset used for transfer learning in GestaltMatcher have become outdated. Moreover, a way to train the model for generating better representation vectors for unseen ultra-rare disorders has not yet been studied. Because of the overall scarcity of patients with ultra-rare disorders, it is infeasible to directly train a model on them. Therefore, we first analyzed the influence of replacing GestaltMatcher DCNN with a state-of-the-art face recognition approach, iResNet with ArcFace. Additionally, we experimented with different face recognition datasets for transfer learning. Furthermore, we proposed test-time augmentation, and model ensembles that mix general face verification models and models specific for verifying disorders to improve the disorder verification accuracy of unseen ultra-rare disorders. Our proposed ensemble model achieves state-of-the-art performance on both seen and unseen disorders.

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