Analyzing temporal developments is crucial for the accurate prognosis of many
medical conditions. Temporal changes that occur over short time scales are key
to assessing the health of physiological functions, such as the cardiac cycle.
Moreover, tracking longer term developments that occur over months or years in
evolving processes, such as age-related macular degeneration (AMD), is
essential for accurate prognosis. Despite the importance of both short and long
term analysis to clinical decision making, they remain understudied in medical
deep learning. State of the art methods for spatiotemporal representation
learning, developed for short natural videos, prioritize the detection of
temporal constants rather than temporal developments. Moreover, they do not
account for varying time intervals between acquisitions, which are essential
for contextualizing observed changes. To address these issues, we propose two
approaches. First, we combine clip-level contrastive learning with a novel
temporal embedding to adapt to irregular time series. Second, we propose
masking and predicting latent frame representations of the temporal sequence.
Our two approaches outperform all prior methods on temporally-dependent tasks
including cardiac output estimation and three prognostic AMD tasks. Overall,
this enables the automated analysis of temporal patterns which are typically
overlooked in applications of deep learning to medicine.

基于医学深度学习，本研究提出了两种方法来分析短期和长期的时间发展，通过结合对比学习和时间嵌入以及遮罩和预测潜在帧表示，实现了对生理功能和疾病进展的自动化分析。