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Related Experiment Video

Updated: Oct 28, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Self-paced and self-consistent co-training for semi-supervised image segmentation.

Ping Wang¹, Jizong Peng¹, Marco Pedersoli¹

¹Department of Software and IT Engineering, Ecole de technologie supérieure, Montreal, H3C1K3, Canada.

Medical Image Analysis

|July 18, 2021

Summary

This summary is machine-generated.

This study introduces a self-paced and self-consistent co-training method to improve semi-supervised image segmentation. The novel approach enhances deep co-training by focusing on easier regions first, boosting performance with scarce annotated data.

Keywords:

Co-training Image segmentation Self-paced learning Semi-supervised learning Temporal ensembling

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Deep co-training is effective for image segmentation with limited labeled data.
Existing semi-supervised segmentation methods require improvement for better performance and robustness.

Purpose of the Study:

To enhance semi-supervised image segmentation using a novel self-paced and self-consistent co-training method.
To improve information distillation from unlabeled images in co-training frameworks.

Main Methods:

Developed a self-paced learning strategy for co-training, prioritizing easier-to-segment regions.
Implemented an end-to-end differentiable loss using generalized Jensen Shannon Divergence (JSD).
Incorporated an uncertainty regularizer based on entropy and a self-ensembling loss for model robustness.

Main Results:

Demonstrated significant performance advantages on challenging image segmentation tasks with limited labeled data.
Outperformed standard co-training baselines and state-of-the-art semi-supervised segmentation approaches.
Showcased effectiveness across diverse image modalities.

Conclusions:

The proposed self-paced and self-consistent co-training method offers a robust and effective solution for semi-supervised image segmentation.
The approach successfully leverages unlabeled data to improve segmentation accuracy, particularly in low-data regimes.