Field-of-view confounding shapes genetic discovery from self-supervised cardiac-imaging phenotypes
· 2026-09-04 · 原文
DOI：10.64898/2026.09.01.26361959v1?rss=1预印本：medRxiv开放获取：绿色OA
Self-supervised models increasingly convert medical images into quantitative phenotypes for biological discovery, but statistical reproducibility does not establish that a learned phenotype represents the intended anatomy. We trained a video masked-autoencoder on 69,932 UK Biobank cardiac cine-MRI studies and performed genome-wide association analysis of its latent representation. Although 18 of 20 leading axes were heritable with well-calibrated statistics, the representation encoded substantial field-of-view information: body size, stature and imaging centre (linear-probe R^2=0.55 for site); standard genomic-control and LD-score diagnostics did not identify this source of phenotype-level confounding. Restricting the field of view to the heart and residualising body and acquisition covari
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1. 人话版
Self-supervised models increasingly convert medical images into quantitative phenotypes for biological discovery, but statistical reproducibility does not establish that a learned phenotype represents the intended anatomy.
We trained a video masked-autoencoder on 69,932 UK Biobank cardiac cine-MRI studies and performed genome-wide association analysis of its latent representation.
2. 领域脉络
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3. 机制拆解
Restricting the field of view to the heart and residualising body and acquisition covari
4. 证据与数字
Although 18 of 20 leading axes were heritable with well-calibrated statistics, the representation encoded substantial field-of-view information: body size, stature and imaging centre (linear-probe R^2=0.55 for site); standard genomic-control and LD-score diagnostics did not identify this source of phenotype-level confounding.
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6. 跨领域连接与意外收获
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