A Semi-Supervised Active Learning Guided Platform for Efficient Medical Image Annotation

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A Semi-Supervised Active Learning Guided Platform for Efficient Medical Image Annotation
Title:
A Semi-Supervised Active Learning Guided Platform for Efficient Medical Image Annotation
Journal Title:
AI Health Summit conference (2023)
DOI:
Publication Date:
23 November 2023
Citation:
N.A.
Abstract:
AI-based clinical imaging annotation platforms aim to ease the annotation burden in deploying deep learning models in clinical settings. Active learning (AL), a human-in-the-loop framework, can optimise model performance under labelling constraints. However, standard, supervised AL models may overfit and bias the sample selection. To address this challenge, we present a novel platform that leverages unlabelled samples through semi-supervised learning (SSL) during training to enhance the effectiveness of active learning.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: I2R
Grant Reference no. : N.A.
Description:
ISSN:
N.A.
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