Daily Arxiv

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Demographic-aware fine-grained classification of pediatric wrist fractures

Created by
  • Haebom

Author

Ammar Ahmed, Ali Shariq Imran, Zenun Kastrati, Sher Muhammad Daudpota

Outline

This paper presents a multifaceted approach to solving the problem of wrist lesion recognition, a common problem in pediatric fracture patients, using a limited dataset. First, we approach wrist lesion recognition as a fine-grained image recognition problem and enhance network performance by integrating patient metadata with X-ray images. Furthermore, we further improve performance by leveraging weights learned from a separate, fine-grained image dataset. While metadata integration has been used in other medical fields, this is the first study to apply it to wrist lesion recognition.

Takeaways, Limitations

Takeaways:
An effective multimodal approach to solving the problem of wrist lesion recognition with a limited dataset is presented.
Identifying the potential for performance enhancement through the fusion of patient metadata and medical images.
Demonstrating the utility of leveraging pre-trained weights from other detailed image datasets.
Presenting a new application potential of metadata integration in the field of wrist lesion recognition.
Limitations:
The dataset used may still be limited in size. Validation with a larger dataset is needed.
Performance may vary depending on the type and quality of metadata. Improvements are needed in metadata selection and preprocessing.
Further validation of the generalizability of the methodology presented in this study is needed. Applicability to various wrist lesions and populations needs to be evaluated.
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