Human Not in the Loop: Objective Sample Difficulty Measures for Curriculum Learning | JoVE Visualize

Area of Science:

Medical imaging analysis
Machine learning in healthcare
Artificial intelligence in radiology

Background:

Curriculum learning trains models using a sample difficulty order, crucial for effective machine learning.
Existing methods for medical image classification rely on subjective human expertise, introducing bias and extra annotation costs.
Objective and automated difficulty measurement is needed for robust curriculum learning in the medical domain.

Purpose of the Study:

To propose and evaluate a novel automated curriculum learning technique for medical image classification.
To introduce variance of gradients (VoG) as an objective metric for sample difficulty.
To assess the effectiveness of VoG-guided curriculum learning on elbow fracture classification from X-ray images.

Main Methods:

Developed an automated curriculum learning approach utilizing variance of gradients (VoG) to quantify sample classification difficulty.
Ranked medical image samples based on VoG scores, with higher scores indicating greater classification difficulty.
Employed VoG-guided curriculum learning for training classification models on X-ray images.
Compared the VoG method against a baseline (no curriculum learning), human-annotated difficulty, and anti-curriculum learning.

Main Results:

The proposed VoG-based curriculum learning achieved comparable and superior performance in both binary and multi-class bone fracture classification tasks.
Demonstrated the efficacy of an automated, objective difficulty measure in enhancing model training.
Outperformed or matched traditional methods, including those relying on human annotations.

Conclusions:

Automated curriculum learning using variance of gradients (VoG) offers an effective and objective alternative to human-guided methods in medical image analysis.
The VoG technique successfully improves classification performance for bone fractures in X-ray images.
This approach reduces reliance on subjective expertise and annotation efforts, paving the way for more efficient AI in medical diagnostics.