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.

翻译：利用医学影像预测未来疾病进展风险具有挑战性，原因在于患者异质性以及影像生物标志物的微妙性或未知性。此外，用于生存分析的深度学习方法易受不同扫描仪间图像域偏移的影响。我们在从视网膜OCT扫描预测晚期干性年龄相关性黄斑变性发病的任务中应对这些问题。我们提出了一种用于生存预测的新型深度学习方法，该方法可从当前扫描联合预测一个与转化时间成反比的风险评分，以及在时间区间$t$内发生转化的概率。该方法通过将偏置项参数化为$t$的函数，生成一族平行超平面。此外，我们开发了基于受试者内图像对的无监督损失函数，以确保风险评分随时间增加，并且未来转化预测与使用未来实际访视扫描进行的AMD分期预测保持一致。此类损失函数使得训练后的模型能够在由不同扫描仪获取的新未标记数据集上进行数据高效微调。在两个由不同扫描仪获取的大型数据集上的广泛评估表明，在6、12和24个月的预测区间内，数据集1的平均AUROC为0.82，数据集2的平均AUROC为0.83。