[Submitted on 6 Jul 2026]
Title:Hierarchical Classification via Cascading Feature Elimination: Application to Human Phenotype Ontology-Aligned Facial Phenotyping (FaceMesh2HPO)
View a PDF of the paper titled Hierarchical Classification via Cascading Feature Elimination: Application to Human Phenotype Ontology-Aligned Facial Phenotyping (FaceMesh2HPO), by Fabio Hellmann and Alexander Hustinx and Benjamin D. Solomon and GestaltMatcher Database Consortium and Tzung-Chien Hsieh and Peter Krawitz and Elisabeth Andr\'e
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Abstract:FaceMesh2HPO is a framework for classifying facial phenotypic descriptors aligned with the Human Phenotype Ontology (HPO) to support clinical diagnosis. Using annotations from 124 clinicians across 10 disorders (107 HPO terms) combined with non-syndromic controls, we generated 3D facial meshes (478 landmarks) from 2D images and trained a hierarchical PointNet-based pipeline with cascading classification and feature elimination. The best models, incorporating 3D meshes, facial outline, and demographic metadata, achieved AUROCs between ~0.55 and ~0.89, with higher performance at parent nodes than leaf terms. External validation showed variable generalizability across disorders. Results demonstrate that hierarchical modeling of 3D facial geometry enables interpretable, ontology-linked phenotype classification, though performance on rare leaf terms remains limited. Improved data diversity and feature selection strategies are needed to enhance robustness and clinical utility.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.05585 [cs.CV]
(or arXiv:2607.05585v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.05585
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Fabio Hellmann [view email] [v1] Mon, 6 Jul 2026 19:33:25 UTC (1,764 KB)
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