---
格式版本: 2
标题: "Multi-Level Evidence Aggregation for Robust Facial Phenotype Retrieval in Rare Genetic Disorder Prioritization"
原文链接: "https://arxiv.org/abs/2608.11037"
发布日期: "2026-08-11"
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发现时间: "2026-08-12T11:26:18+08:00"
入库时间: "2026-08-12T03:29:03.639Z"
来源平台: "arXiv 学术论文搜索"
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去重键: "https://arxiv.org/abs/2608.11037"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:Multi-Level Evidence Aggregation for Robust Facial Phenotype Retrieval in Rare Genetic Disorder Prioritization

[View PDF](https://arxiv.org/pdf/2608.11037) [HTML (experimental)](https://arxiv.org/html/2608.11037v1)

> Abstract:AI-assisted facial phenotyping supports rare genetic disorder prioritization by retrieving visually similar diagnosed cases from facial image reference databases such as the GestaltMatcher Database (GMDB). Existing GestaltMatcher-based retrieval frameworks compare each test image with individual gallery images in a facial phenotype embedding space. However, this pointwise formulation does not fully exploit available evidence, because patients may have multiple images and disorders may be represented by multiple diagnosed gallery patients.  
> We propose an inference-time multi-level evidence aggregation framework that improves facial phenotype retrieval without modifying the underlying GestaltMatcher-Arc encoder. The framework combines embedding-level patient aggregation of multiple images from the same individual, patient-weighted disorder centroids, and hybrid individual-centroid scoring to integrate test-patient observations, disorder-level gallery evidence, and local nearest-neighbor evidence.  
> We evaluated the approach on GMDB v1.1.4 across disorders represented during training (GMDB-Freq), unseen disorders (GMDB-Rare), and multi-image patient subsets, using a unified gallery containing both GMDB-Freq and GMDB-Rare disorders. Multi-level evidence aggregation improved mean per-disorder top-$N$ retrieval accuracy across all evaluation subsets. Top-1 accuracy increased from 38.52% to 48.82% on GMDB-Freq and from 19.38% to 23.79% on GMDB-Rare. On multi-image subsets, top-1 accuracy increased from 46.12% to 60.94% on GMDB-Multi-Freq and from 18.54% to 26.71% on GMDB-Multi-Rare.  
> These findings show that inference-time aggregation can improve next-generation facial phenotype retrieval without retraining the encoder, supporting a shift from isolated single-image matching toward multi-level aggregation of patient and disorder evidence for rare-disorder prioritization.

| Comments: |  |
| --- | --- |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR) |
| Cite as: | [arXiv:2608.11037](https://arxiv.org/abs/2608.11037) \[cs.CV\] |
|  | (or [arXiv:2608.11037v1](https://arxiv.org/abs/2608.11037v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11037](https://doi.org/10.48550/arXiv.2608.11037) |

## Submission history

From: Alexander Hustinx \[[view email](https://arxiv.org/show-email/2e0b3a44/2608.11037)\]  
**\[v1\]** Tue, 11 Aug 2026 15:12:44 UTC (1,202 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11037) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
