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GestaltMatcher have become outdated.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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