Automatic classification of prostate MR series type using image content and metadata
<|reference_start|>Automatic classification of prostate MR series type using image content and metadata: With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial intelligence-based analysis. However, incomplete or missing metadata prevents effective automation. We therefore propose a deep-learning method for classification of prostate cancer scanning sequences based on a combination of image data and DICOM metadata. We demonstrate superior results compared to metadata or image data alone, and make our code publicly available at https://github.com/deepakri201/DICOMScanClassification.<|reference_end|>
@article{krishnaswamy2024automatic,
    title={Automatic classification of prostate MR series type using image content
  and metadata},
    author={Deepa Krishnaswamy, B'alint Kov'acs, Stefan Denner, Steve Pieper,
  David Clunie, Christopher P. Bridge, Tina Kapur, Klaus H. Maier-Hein, Andrey
  Fedorov},
    journal={arXiv preprint arXiv:2404.10892},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10892},
    primaryClass={eess.IV cs.CV}
}