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AuthorAhsan, Muhammad Ahtazaz
AuthorQayyum, Adnan
AuthorRazi, Adeel
AuthorQadir, Junaid
Available date2023-07-13T05:40:53Z
Publication Date2022
Publication NameMedical and Biological Engineering and Computing
ResourceScopus
ISSN1400118
URIhttp://dx.doi.org/10.1007/s11517-022-02633-w
URIhttp://hdl.handle.net/10576/45591
AbstractIn recent years, deep learning (DL) techniques have provided state-of-the-art performance in medical imaging. However, good quality (annotated) medical data is in general hard to find due to the usually high cost of medical images, limited availability of expert annotators (e.g., radiologists), and the amount of time required for annotation. In addition, DL is data-hungry and its training requires extensive computational resources. Furthermore, DL being a black-box method lacks transparency on its inner working and lacks fundamental understanding behind decisions made by the model and consequently, this notion enhances the uncertainty on its predictions. To this end, we address these challenges by proposing a hybrid model, which uses a Bayesian convolutional neural network (BCNN) for uncertainty quantification, and an active learning approach for annotating the unlabeled data. The BCNN is used as a feature descriptor and these features are then used for training a model, in an active learning setting. We evaluate the proposed framework for diabetic retinopathy classification problem and demonstrate state-of-the-art performance in terms of different metrics. Graphical abstract: [Figure not available: see fulltext.]. 2022, International Federation for Medical and Biological Engineering.
SponsorAdeel Razi is affiliated with The Wellcome Centre for Human Neuroimaging supported by core funding from Wellcome (203147/Z/16/Z).
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectActive learning
Deep learning
Diabetic retinopathy
Uncertainty quantification
TitleAn active learning method for diabetic retinopathy classification with uncertainty quantification
TypeArticle
Pagination2797-2811
Issue Number10
Volume Number60
dc.accessType Abstract Only


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