An active learning method for diabetic retinopathy classification with uncertainty quantification

Muhammad Ahtazaz Ahsan, Adnan Qayyum, Adeel Razi, Junaid Qadir

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)


In 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.].

Original languageEnglish
Pages (from-to)2797-2811
Number of pages15
JournalMedical & Biological Engineering & Computing
Issue number10
Publication statusPublished - Oct 2022


  • Active learning
  • Deep learning
  • Diabetic retinopathy
  • Uncertainty quantification

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