Bayesian Deep Active Learning for Medical Image Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12721)


Deep Learning has achieved a state-of-the-art performance in medical imaging analysis but requires a large number of labelled images to obtain good adequate performance. However, such labelled images are costly to acquire in time, labour, and human expertise. We propose a novel practical Bayesian Active Learning approach using Dropweights and overall bias-corrected uncertainty measure to suggest which unlabelled image to annotate. Experiments were done on Brain Tumour MR images, Microscopic Cell Image classification, Fluoro-chromogenic cytokeratin-Ki-67 double staining cancer images and Retina fundus image segmentation tasks. We demonstrate that our active learning technique is equally successful or better than other existing active learning approaches in high dimensional data to reduce the image labelling effort significantly. We believe Bayesian deep active learning framework with very few annotated samples in a practical way will benefit clinicians to obtain fast and accurate image annotation with confidence.


Bayesian Active Learning Bias-corrected uncertainty Dropweights Image annotation Semantic segmentation classification 


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© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Brunel University LondonUxbridgeUK

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