  Forecasting Parapapillary atrophy (PPA), i.e., a symptom related to most
irreversible eye diseases, provides an alarm for implementing an intervention
to slow down the disease progression at early stage. A key question for this
forecast is: how to fully utilize the historical data (e.g., retinal image) up
to the current stage for future disease prediction? In this paper, we provide
an answer with a novel framework, namely \textbf{D}isease \textbf{F}orecast via
\textbf{P}rogression \textbf{L}earning (\textbf{DFPL}), which exploits the
irreversibility prior (i.e., cannot be reversed once diagnosed). Specifically,
based on this prior, we decompose two factors that contribute to the prediction
of the future disease: i) the current disease label given the data (retinal
image, clinical attributes) at present and ii) the future disease label given
the progression of the retinal images that from the current to the future. To
model these two factors, we introduce the current and progression predictors in
DFPL, respectively. In order to account for the degree of progression of the
disease, we propose a temporal generative model to accurately generate the
future image and compare it with the current one to get a residual image. The
generative model is implemented by a recurrent neural network, in order to
exploit the dependency of the historical data. To verify our approach, we apply
it to a PPA in-house dataset and it yields a significant improvement
(\textit{e.g.}, \textbf{4.48\%} of accuracy; \textbf{3.45\%} of AUC) over
others. Besides, our generative model can accurately localize the
disease-related regions.
