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

arXiv: https://arxiv.org/abs/2211.06764 · HTML: https://mlsanity.com/paper/2211.06764/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 12 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Partial FC: Training 10 Million Identities on a Single Machine](https://mlsanity.com/paper/2010.05222/) (2020)
- [Improved Residual Networks for Image and Video Recognition](https://mlsanity.com/paper/2004.04989/) (2020)

## Built on by (most cited first, 4 of 4)

- [GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Text](https://mlsanity.com/paper/2312.15320/) (2023)
- [Multimodal Integrated Knowledge Transfer to Large Language Models through Preference Optimization with Biomedical Applications](https://mlsanity.com/paper/2505.05736/) (2025)
- [Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning](https://mlsanity.com/paper/2509.10635/) (2025)
- [Multi-Level Evidence Aggregation for Robust Facial Phenotype Retrieval in Rare Genetic Disorder Prioritization](https://mlsanity.com/paper/2608.11037/) (2026)

## Abstract (arXiv)

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.