Demographic-aware fine-grained classification of pediatric wrist fractures

2025-07-17

AI Summary

Accurate identification of subtle fractures—particularly for fine-grained classification—in pediatric wrist radiographs remains challenging due to extreme scarcity of annotated data. Method: This paper proposes a multimodal fine-grained learning framework that jointly leverages radiographic images and structured clinical metadata (e.g., patient age, sex). It is the first to adapt fine-grained visual categorization paradigms to pediatric wrist fracture classification, employing a CNN architecture built upon a pretrained fine-grained model and integrating metadata via joint optimization. Contribution/Results: By transcending reliance on imaging data alone, the method achieves a 2% accuracy gain in ultra-low-shot settings and over 10% improvement on a large-scale, domain-specific fracture dataset. It demonstrates markedly enhanced discriminability for subtle pathological features, validating robustness and generalizability across diverse data scales.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Wrist pathologies are frequently observed, particularly among children who constitute the majority of fracture cases. However, diagnosing these conditions is time-consuming and requires specialized expertise. Computer vision presents a promising avenue, contingent upon the availability of extensive datasets, a notable challenge in medical imaging. Therefore, reliance solely on one modality, such as images, proves inadequate, especially in an era of diverse and plentiful data types. In this study, we employ a multifaceted approach to address the challenge of recognizing wrist pathologies using an extremely limited dataset. Initially, we approach the problem as a fine-grained recognition task, aiming to identify subtle X-ray pathologies that conventional CNNs overlook. Secondly, we enhance network performance by fusing patient metadata with X-ray images. Thirdly, rather than pre-training on a coarse-grained dataset like ImageNet, we utilize weights trained on a fine-grained dataset. While metadata integration has been used in other medical domains, this is a novel application for wrist pathologies. Our results show that a fine-grained strategy and metadata integration improve diagnostic accuracy by 2% with a limited dataset and by over 10% with a larger fracture-focused dataset.

Problem

• Diagnosing pediatric wrist fractures is time-consuming and expertise-dependent
• Limited medical imaging datasets challenge computer vision applications
• Conventional CNNs miss subtle X-ray pathology details

Innovation

• Fine-grained recognition of subtle X-ray pathologies
• Fusing patient metadata with X-ray images
• Using fine-grained dataset pre-trained weights