基於影像的不確定性感知剩餘壽命預測

摘要

從圖像預測與死亡率相關的結果，提供了可及性高、非侵入性且可擴展的健康篩查前景。我們提出了一種方法，利用預訓練的視覺變換器基礎模型，從面部及全身圖像中估算剩餘壽命，並進行穩健的不確定性量化。我們展示了預測不確定性會隨著真實剩餘壽命而系統性地變化，且這種不確定性可通過為每個樣本學習高斯分佈來有效建模。我們的方法在一個已建立的數據集上達到了7.48年的平均絕對誤差（MAE）的頂尖水平，並在本研究整理並發布的兩個更高質量的新數據集上進一步將MAE提升至4.79年和5.07年。重要的是，我們的模型提供了校準良好的不確定性估計，如分桶預期校準誤差為0.62年所示。雖然這些結果並非旨在臨床應用，但它們凸顯了從圖像中提取醫學相關信號的潛力。我們公開了所有代碼和數據集，以促進進一步的研究。

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 7.48 years on an established Dataset, and further improves to 4.79 and 5.07 years MAE on two new, higher-quality datasets curated and published in this work. Importantly, our models provide well-calibrated uncertainty estimates, as demonstrated by a bucketed expected calibration error of 0.62 years. 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.

基於影像的不確定性感知剩餘壽命預測

Uncertainty-Aware Remaining Lifespan Prediction from Images

摘要

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