Subclass-Aware Contrastive Semi-Supervised Learning for Inflammatory Bowel Disease Classification from Colonoscopy Images | JoVE Visualize

Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Accurate classification of Inflammatory Bowel Disease (IBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC), from colonoscopy images is crucial for patient diagnosis and treatment.
Deep learning models for IBD classification require substantial labeled data, which is often scarce, posing a significant challenge.
Existing semi-supervised learning methods using pseudo-labeling struggle with IBD images due to high intra-class variability and subtle inter-class differences, leading to inaccurate pseudo-labels and confirmation bias.

Purpose of the Study:

To develop an advanced semi-supervised learning method, Subclass-Aware Contrastive Semi-Supervised Learning (SACSSL), for accurate IBD classification from colonoscopy images.
To overcome the limitations of traditional pseudo-labeling methods by mitigating confirmation bias and handling intra-class heterogeneity.
To enhance the performance of deep learning models for IBD classification using limited labeled data.

Main Methods:

Proposed SACSSL method integrates a subclass-aware contrastive module into a pseudo-labeling framework (e.g., FixMatch).
Uncertain unlabeled samples are processed with instance-level contrastive loss to reduce confirmation bias.
Confident unlabeled samples are clustered into fine-grained subclasses using prototypes and supervised contrastive loss to improve inter-class separability and intra-class diversity.

Main Results:

SACSSL achieved state-of-the-art performance on both an in-house IBD classification dataset (Daping) and a public UC severity grading dataset (LIMUC) in a semi-supervised setting.
With only 20% labeled data, SACSSL reached 93.2% accuracy and 80.1% F1-score on the Daping dataset, closely approaching the fully supervised performance.
On the LIMUC dataset, SACSSL achieved 76.4% accuracy and 68.9% F1-score, demonstrating its effectiveness across different IBD-related tasks.

Conclusions:

The proposed SACSSL method effectively enhances semi-supervised colonoscopy image classification for IBD.
SACSSL successfully addresses challenges of inaccurate pseudo-labels, confirmation bias, and intra-class variability in IBD image analysis.
The method demonstrates significant potential for improving IBD diagnosis and treatment stratification with limited labeled data.