Work

Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

Year: 2021

Type: article

Abstract: We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

Source: Journal of Imaging

DOI: 10.3390/jimaging7020037

Authors: Isah Charles Saidu, Lehel Csató

Institutions: African Institute of Science and Technology, Babeș-Bolyai University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,728

Funders: African University of Science and Technology, Eötvös Loránd Tudományegyetem