用图像预测剩余寿命，还能给出可信的不确定性估计。
Uncertainty-Aware Remaining Lifespan Prediction from Images
- 基于预训练视觉模型，从人脸和全身图像中预测寿命
- 在新数据集上实现4.91~4.99年的预测误差，优于现有方法
- 提供校准后的不确定性估计，适合研究健康风险评估
从图像中预测与死亡相关的结果，有望实现可及、无创且可扩展的健康筛查。本文提出一种方法，利用预训练的视觉变换器基础模型，从面部和全身图像中估计剩余寿命，并实现稳健的不确定性量化。我们发现预测不确定性随真实剩余寿命系统性变化，且可通过为每一样本学习高斯分布有效建模。该方法在已有数据集上达到7.41年的均值绝对误差（MAE），在本文新构建并发布的两个高质量数据集上分别达到4.91年和4.99年的MAE。重要的是，模型提供校准的不确定性估计，在人脸数据集上的分桶期望校准误差为0.82年。尽管不用于临床部署，这些结果凸显了从图像中提取医学相关信号的潜力。所有代码和数据集均已公开，以促进进一步研究。
原文摘要 · Abstract (English)
Predicting mortality-related outcomes from images offers the prospect of accessible, noninvasive, and scalable health screening. We present a method that leverages pretrained vision transformer foundation models to estimate remaining lifespan from facial and whole-body images, alongside robust uncertainty quantification. We show that predictive uncertainty varies systematically with the true remaining lifespan, and that this uncertainty can be effectively modeled by learning a Gaussian distribution for each sample. Our approach achieves state-of-the-art mean absolute error (MAE) of 7.41 years on an established dataset, and further achieves 4.91 and 4.99 years MAE on two new, higher-quality datasets curated and published in this work. Importantly, our models provide calibrated uncertainty estimates, as demonstrated by a bucketed expected calibration error of 0.82 years on the Faces Dataset. While not intended for clinical deployment, these results highlight the potential of extracting medically relevant signals from images. We make all code and datasets available to facilitate further research.
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