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Abstract #3404

Knowledge-based Labeled Data Selection in Semi-Supervised Learning

Ashish Saxena1, Vanika Singhal1, Chitresh Bhushan2, and Dattesh Dayanand Shanbhag1
1GE Healthcare, Bangalore, India, 2GE Healthcare, Niskayuna, NY, United States

Synopsis

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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Keywords