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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation.

Isah Charles Saidu¹, Lehel Csató²

¹Department of Computer Science, African University of Science and Technology, Abuja, Nigeria.

Journal of Imaging

|August 30, 2021

Summary

This summary is machine-generated.

Active Bayesian UNet (AB-UNet) improves medical image segmentation efficiency. This method uses active learning and Bayesian principles to achieve stable training and better generalization with less data annotation.

Keywords:

AB-UNet Bayesian learning Bayesian, active learning convolutional networks stochastic gradient descent

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Area of Science:

Medical Image Analysis
Machine Learning
Artificial Intelligence

Background:

Image segmentation is crucial for medical image analysis.
Current methods often require extensive annotated data, limiting efficiency.
Deep learning models like UNet show promise but need optimization for sample efficiency.

Purpose of the Study:

To introduce a sample-efficient image segmentation method using active learning.
To leverage Bayesian principles within a UNet architecture for enhanced performance.
To reduce the annotation effort required for medical image segmentation.

Main Methods:

Developed Active Bayesian UNet (AB-UNet), a convolutional neural network.
Incorporated batch normalization and max-pool dropout for regularization.
Utilized the probabilistic extension of dropout to exploit inherent system uncertainty.
Employed active learning to efficiently select informative samples from unlabeled data.

Main Results:

AB-UNet demonstrated stable training across various medical image datasets.
Achieved better generalization performance compared to standard methods with reduced annotation.
Showcased efficient selection of samples from unlabeled datasets for annotation.

Conclusions:

AB-UNet offers a sample-efficient approach to medical image segmentation.
The method effectively reduces the need for extensive data annotation.
Bayesian deep learning with active learning enhances segmentation model performance and generalizability.