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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 for diagnosing wrist lesions, a common finding in pediatric fracture patients. To address the lack of medical image data, we fuse X-ray images with patient metadata and define the problem as a fine-grained recognition task utilizing pre-trained weights on a fine-grained dataset rather than a general dataset such as ImageNet. Unlike previous studies, this is the first to apply metadata integration to wrist lesion recognition, demonstrating a 2% improvement in diagnostic accuracy on a small, customized dataset and over 10% improvement on a large-scale fracture dataset.

Takeaways, Limitations

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Takeaways:
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X-Suggesting the possibility of improving the accuracy of wrist lesion diagnosis by integrating ray images and patient metadata.
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Demonstrating the effectiveness of a fine-grained dataset and a transformer-based approach.
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Providing a practical solution to the problem of lack of medical imaging data.
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Limitations:
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The datasets used are limited to a small, custom dataset and a large fracture dataset. Validation with a more diverse and larger dataset is needed.
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A more in-depth analysis of the effectiveness of metadata integration is needed. Additional research is needed to understand how specific metadata elements contribute to improved accuracy.
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Further research is needed to determine generalizability to other wrist lesion types or populations.
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