Deep Bayesian baseline for segmenting diabetic retinopathy lesions: Advances and challenges

Azat Garifullin, Lasse Lensu, Hannu Uusitalo

    Research output: Contribution to journalArticleScientificpeer-review

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    Abstract

    Early diagnosis of retinopathy is essential for preventing retinal complications and visual impairment due to diabetes. For the detection of retinopathy lesions from retinal images, several automatic approaches based on deep neural networks have been developed in the recent years. Most of the proposed methods produce point estimates of pixels belonging to the lesion areas and give no or little information on the uncertainty of method predictions. However, the latter can be essential in the examination of the medical condition of the patient when the goal is early detection of abnormalities. This work extends the recent research with a Bayesian framework by considering the parameters of a convolutional neural network as random variables and utilizing stochastic variational dropout based approximation for uncertainty quantification. The framework includes an extended validation procedure and it allows analyzing lesion segmentation distributions, model calibration and prediction uncertainties. Also the challenges related to the deep probabilistic model and uncertainty quantification are presented. The proposed method achieves area under precision-recall curve of 0.84 for hard exudates, 0.641 for soft exudates, 0.593 for haemorrhages, and 0.484 for microaneurysms on IDRiD dataset.

    Original languageEnglish
    Article number104725
    JournalComputers in Biology and Medicine
    Volume136
    DOIs
    Publication statusPublished - 2021
    Publication typeA1 Journal article-refereed

    Keywords

    • Bayesian deep learning
    • Diabetic retinopathy
    • Haemorrhage
    • Hard exudate
    • Lesion segmentation
    • Microaneurysm
    • Soft exudate

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Computer Science Applications
    • Health Informatics

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