Few-shot meta-learning for recognizing facial phenotypes of genetic disorders
- Computer vision-based methods have valuable use cases in precision medicine, and recognizing facial phenotypes of genetic disorders is one of them. Many genetic disorders are known to affect faces' visual appearance and geometry. Automated classification and similarity retrieval aid physicians in decision-making to diagnose possible genetic conditions as early as possible. Previous work has addressed the problem as a classification problem and used deep learning methods. The challenging issue in practice is the sparse label distribution and huge class imbalances across categories. Furthermore, most disorders have few labeled samples in training sets, making representation learning and generalization essential to acquiring a reliable feature descriptor. In this study, we used a facial recognition model trained on a large corpus of healthy individuals as a pre-task and transferred it to facial phenotype recognition. Furthermore, we created simple baselines of few-shot meta-learningComputer vision-based methods have valuable use cases in precision medicine, and recognizing facial phenotypes of genetic disorders is one of them. Many genetic disorders are known to affect faces' visual appearance and geometry. Automated classification and similarity retrieval aid physicians in decision-making to diagnose possible genetic conditions as early as possible. Previous work has addressed the problem as a classification problem and used deep learning methods. The challenging issue in practice is the sparse label distribution and huge class imbalances across categories. Furthermore, most disorders have few labeled samples in training sets, making representation learning and generalization essential to acquiring a reliable feature descriptor. In this study, we used a facial recognition model trained on a large corpus of healthy individuals as a pre-task and transferred it to facial phenotype recognition. Furthermore, we created simple baselines of few-shot meta-learning methods to improve our base feature descriptor. Our quantitative results on GestaltMatcher Database show that our CNN baseline surpasses previous works, including GestaltMatcher, and few-shot meta-learning strategies improve retrieval performance in frequent and rare classes.…
Author: | Ömer SümerORCiDGND, Fabio HellmannORCiDGND, Alexander Hustinx, Tzung-Chien Hsieh, Elisabeth AndréORCiDGND, Peter Krawitz |
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URN: | urn:nbn:de:bvb:384-opus4-1005931 |
Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/100593 |
ISBN: | 978-1-64368-388-1OPAC |
Parent Title (English): | Caring is sharing – exploiting the value in data for health and innovation: proceedings of MIE 2023 |
Publisher: | IOS Press |
Place of publication: | Amsterdam |
Editor: | Maria Hägglund, Madeleine Blusi, Stefano Bonacina, Lina Nilsson, Inge Cort Madsen, Sylvia Pelayo, Anne Moen, Arriel Benis, Lars Lindsköld, Parisis Gallos |
Type: | Conference Proceeding |
Language: | English |
Year of first Publication: | 2023 |
Publishing Institution: | Universität Augsburg |
Release Date: | 2022/12/22 |
First Page: | 932 |
Last Page: | 936 |
Series: | Studies in Health Technology and Informatics ; 302 |
DOI: | https://doi.org/10.3233/shti230312 |
Institutes: | Fakultät für Angewandte Informatik |
Fakultät für Angewandte Informatik / Institut für Informatik | |
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Menschzentrierte Künstliche Intelligenz | |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Licence (German): | CC-BY-NC 4.0: Creative Commons: Namensnennung - Nicht kommerziell (mit Print on Demand) |