Keywords: Analysis/Processing, AI/ML Software, Semi-supervised learning
Motivation: To reach the optimal decision boundary in semi-supervised model learning, labelled data should have all the data diversity representation.
Goal(s): To evaluate the impact of knowledge-based labeled data selection in semi-supervised learning of spine vertebrae segmentation model.
Approach: We train semi-supervised learning model for using random and knowledge-based labeled data selection method and compared the model performance using Dice score and Hausdorff distance.
Results: For metal implant cases, our semi-supervised model trained on knowledge-based labelled data selection, shows an increase of 5% in Dice score and 35% reduction in the Hausdorff distance metric.
Impact: We present the importance of data diversity representation in the labelled data of semi-supervised model learning.
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