Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture Classification.

Area of Science:

• Radiology
• Medical Imaging
• Artificial Intelligence

Background:

• Elbow fracture diagnosis typically requires multiple X-ray views (frontal and lateral).
• Accurate classification of elbow fracture subtypes is crucial for effective treatment.

Purpose of the Study:

• To propose a novel multiview deep learning method for elbow fracture subtype classification.
• To enhance diagnostic performance by integrating quantitative medical knowledge and transfer learning.

Main Methods:

• Developed a multiview deep learning network leveraging transfer learning from single-view models (frontal and lateral).
• Integrated quantitative medical knowledge using a curriculum learning framework for progressive sample difficulty.
• The network supports both dual-view and single-view input for flexibility.

Main Results:

• The proposed multiview method demonstrated superior performance compared to two related bone fracture classification methods.
• Extensive experiments on a dataset of 1,964 elbow X-ray images validated the method's effectiveness.
• The technique showed potential to boost the performance of existing comparative methods.

Conclusions:

• The developed multiview deep learning approach offers an effective solution for elbow fracture subtype classification.
• The integration of transfer learning and curriculum learning enhances diagnostic accuracy in medical imaging.
• This method provides a flexible and high-performing tool for radiological fracture assessment.