用平行超平面建模视网膜OCT图像，预测干性老年黄斑变性进展风险。
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCT
- 设计平行超平面结构，联合预测转换风险与时间概率。
- 跨扫描仪数据下平均AUROC达0.82~0.83，性能稳定。
- 无监督损失实现新设备数据的高效微调，适合临床部署。
从医学影像预测未来疾病进展风险面临患者异质性及潜在影像生物标志物未知的挑战。此外，深度学习生存分析方法易受不同扫描仪间的图像域偏移影响。本文针对干性老年黄斑变性（dAMD）的早期预测问题，提出一种新型深度学习生存预测方法，从当前OCT扫描中联合预测风险评分（与转换时间呈反比）以及在时间区间$ t $内的转换概率。该方法通过将偏置项参数化为$ t $的函数，生成一组平行超平面。同时，设计基于同个患者图像对的无监督损失，确保风险评分随时间递增，并使未来转换预测与实际后续随访扫描的AMD分期一致。这些损失可实现模型在新扫描仪采集的未标注数据上的数据高效微调。在两个不同扫描仪采集的大规模数据集上评估，6、12、24个月预测区间下的平均AUROC分别为0.82（数据集1）和0.83（数据集2）。
原文摘要 · Abstract (English)
Predicting future disease progression risk from medical images is challenging due to patient heterogeneity, and subtle or unknown imaging biomarkers. Moreover, deep learning (DL) methods for survival analysis are susceptible to image domain shifts across scanners. We tackle these issues in the task of predicting late dry Age-related Macular Degeneration (dAMD) onset from retinal OCT scans. We propose a novel DL method for survival prediction to jointly predict from the current scan a risk score, inversely related to time-to-conversion, and the probability of conversion within a time interval $t$. It uses a family of parallel hyperplanes generated by parameterizing the bias term as a function of $t$. In addition, we develop unsupervised losses based on intra-subject image pairs to ensure that risk scores increase over time and that future conversion predictions are consistent with AMD stage prediction using actual scans of future visits. Such losses enable data-efficient fine-tuning of the trained model on new unlabeled datasets acquired with a different scanner. Extensive evaluation on two large datasets acquired with different scanners resulted in a mean AUROCs of 0.82 for Dataset-1 and 0.83 for Dataset-2, across prediction intervals of 6,12 and 24 months.
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