Forecasting Irreversible Disease via Progression Learning

• URL: http://arxiv.org/abs/2012.11107v2
• Date: Tue, 13 Apr 2021 08:59:01 GMT
• Title: Forecasting Irreversible Disease via Progression Learning
• Authors: Botong Wu, Sijie Ren, Jing Li, Xinwei Sun, Shiming Li, Yizhou Wang
• Abstract summary: Parapapillary atrophy (PPA) is a symptom related to most irreversible eye diseases. We propose a novel framework, namely textbfDisease textbfForecast via textbfProgression textbfLearning (textbfDFPL)
• Score: 18.13106500847306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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