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Supervised representation learning based on various levels of pediatric radiographic views for transfer learning
Sunggu Kyung1, Miso Jang2, Seungju Park2
1Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea.
Scientific Reports
|March 30, 2024
Summary
This study introduces PedXnets, novel pre-trained models for pediatric X-ray analysis, outperforming existing methods in fracture classification and bone age assessment. These models enhance medical image analysis by focusing on relevant regions of interest.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Transfer learning is crucial for medical AI but often uses natural image models unsuitable for grayscale medical data.
- Pediatric radiography datasets are scarce, hindering the development of specialized AI models.
Purpose of the Study:
- To develop and validate novel pre-trained models (PedXnets) specifically for pediatric X-ray analysis.
- To assess the effectiveness of transfer learning using PedXnets on downstream pediatric tasks.
Main Methods:
- Constructed class-balanced pediatric radiograph datasets (PedXnets) from 70,000 X-ray images spanning 24 years.
- Pre-trained Inception V3 models with varying classification layers (7C, 30C, 68C) on PedXnets.
- Evaluated transfer learning performance on fracture classification and bone age assessment (BAA).
Main Results:
- PedXnets demonstrated superior performance in quantitative assessments for both classification and regression tasks.
- Visual analyses confirmed that PedXnets focused on clinically relevant regions of interest.
- Validated the transferability and positive effects of transfer learning in pediatric medical imaging.
Conclusions:
- PedXnets provide effective pre-trained models for pediatric X-ray analysis, addressing limitations of natural image-based transfer learning.
- The developed models significantly enhance performance in critical pediatric diagnostic tasks like fracture detection and BAA.
- This work establishes a foundation for improved AI-driven diagnostic tools in pediatric radiology.
