Uncertainty aware and explainable diagnosis of retinal disease

Amitojdeep Singh; Sourya Sengupta; Mohamed Abdul Rasheed; Varadharajan Jayakumar; Vasudevan Lakshminarayanan

doi:10.1117/12.2581362

15 February 2021 Uncertainty aware and explainable diagnosis of retinal disease

Amitojdeep Singh, Sourya Sengupta, Mohamed Abdul Rasheed, Varadharajan Jayakumar, Vasudevan Lakshminarayanan

Proceedings Volume 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications; 116010J (2021) https://doi.org/10.1117/12.2581362
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Deep learning methods for ophthalmic diagnosis have shown considerable success in tasks like segmentation and classification. However, their widespread application is limited due to the models being opaque and vulnerable to making a wrong decision in complicated cases. Explainability methods show the features that a system used to make prediction while uncertainty awareness is the ability of a system to highlight when it is not sure about the decision. This is one of the first studies using uncertainty and explanations for informed clinical decision making. We perform uncertainty analysis of a deep learning model for diagnosis of four retinal diseases - age-related macular degeneration (AMD), central serous retinopathy (CSR), diabetic retinopathy (DR), and macular hole (MH) using images from a publicly available (OCTID) dataset. Monte Carlo (MC) dropout is used at the test time to generate a distribution of parameters and the predictions approximate the predictive posterior of a Bayesian model. A threshold is computed using the distribution and uncertain cases can be referred to the ophthalmologist thus avoiding an erroneous diagnosis. The features learned by the model are visualized using a proven attribution method from a previous study. The effects of uncertainty on model performance and the relationship between uncertainty and explainability are discussed in terms of clinical significance. The uncertainty information along with the heatmaps make the system more trustworthy for use in clinical settings.

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Citation Download Citation

Amitojdeep Singh, Sourya Sengupta, Mohamed Abdul Rasheed, Varadharajan Jayakumar, and Vasudevan Lakshminarayanan "Uncertainty aware and explainable diagnosis of retinal disease", Proc. SPIE 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 116010J (15 February 2021); https://doi.org/10.1117/12.2581362

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