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This is a Quantitative Study study.

September 17, 2025

Efficient Utilization of Unlabeled Data during Training of Semi-Supervised Learning Model

Puntos clave

The proposed training method improved segmentation performance with a Dice Score of 0.86, indicating better efficiency in using unlabeled data.
In contrast to conventional methods, which balance labeled and unlabeled samples, unbalanced sampling can enhance model training and performance.
The innovative semi-supervised model training methodology effectively utilizes all available unlabeled data in each training epoch.
An improvement in segmentation performance was observed, as the new approach yielded a better result than the balanced sampling method.

Resumen

Motivation: Current simultaneous semi-supervised training method uses an equal number of labeled and unlabeled data samples (balanced sampling) in each epoch, which seems to be an inefficient utilization of all the unlabelled data. Goal(s): To develop a training approach that efficiently utilizes all the unlabeled dataset in each training epoch. Approach: We propose a training method that first uses a balanced sampling to train the model, followed by training using unbalanced sampling. Results: We report an improvement in the segmentation performance with the proposed training approach against conventional training approach (Dice Score: 0.86 ± 0.02 versus 0.84 ± 0.04, p<0.001). Impact: We propose a simultaneous semi-supervised model training methodology that ensures efficient utilization of all the available unlabeled data in each training epoch.

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