Demographic-aware fine-grained classification of pediatric wrist fractures
It addresses a domain-specific medical imaging problem for pediatric wrist fractures, with incremental improvements through metadata integration.
This study tackled pediatric wrist fracture classification by integrating patient metadata with X-rays using a fine-grained transformer approach and fine-grained pre-training, improving diagnostic accuracy by 2% on a small dataset and over 10% on a larger dataset.
Wrist pathologies are frequently observed, particularly among children who constitute the majority of fracture cases. 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. This study addresses the problem using a multifaceted approach: framing it as a fine-grained recognition task, fusing patient metadata with X-rays, and leveraging weights from a separate fine-grained dataset rather than from a coarse-grained dataset like ImageNet. Unlike prior work, this is the first application of metadata integration for wrist pathology recognition. Our results show that combining fine-grained transformer approach, fine-grained pre-training, and metadata integration improves diagnostic accuracy by 2% on small custom curated dataset and over 10% on a larger fracture dataset.