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Improving Colonoscopy Lesion Classification Using Semi-Supervised Deep Learning.

Mayank Golhar¹, Taylor L Bobrow², Mirmilad Pourmousavi Khoshknab³

¹Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

IEEE Access : Practical Innovations, Open Solutions

|March 22, 2021

Summary

Semi-supervised learning, using an unsupervised jigsaw task, significantly improves colonoscopy image analysis. This approach enhances lesion classification accuracy and domain adaptation compared to fully-supervised methods.

Keywords:

Colonoscopy deep learning domain adaptation endoscopy jigsaw lesion classification out-of-distribution detection semi-supervised unsupervised

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Area of Science:

Medical image analysis
Computer vision
Machine learning

Background:

Data-driven image analysis is often limited by insufficient annotated training data.
Semi-supervised learning leverages unlabeled data to create robust image representations, improving supervised task performance.

Purpose of the Study:

To evaluate the effectiveness of unsupervised jigsaw learning combined with supervised training for colonoscopy image analysis.
To benchmark improvements in domain adaptation and out-of-distribution detection using semi-supervised learning.

Main Methods:

Implemented an unsupervised jigsaw learning task for representation learning.
Combined unsupervised pre-training with supervised training for lesion classification in colonoscopy images.
Assessed performance on domain adaptation and out-of-distribution detection tasks.

Main Results:

Achieved up to a 9.8% improvement in lesion classification accuracy compared to a fully-supervised baseline.
Demonstrated superior performance of semi-supervised learning over supervised learning in domain adaptation.
Showcased enhanced out-of-distribution detection capabilities with the semi-supervised approach.

Conclusions:

Unsupervised jigsaw learning combined with supervised methods offers a powerful strategy for improving colonoscopy image analysis.
Semi-supervised learning is crucial for addressing challenges like limited labeled data and diverse endoscopy systems in clinical practice.
The proposed method enhances diagnostic accuracy and robustness in real-world colonoscopy applications.