Automatic classification of prostate MR series type using image content
  and metadata
Deepa Krishnaswamy Bálint Kovács Stefan Denner Steve Pieper David Clunie Christopher P. Bridge Tina Kapur Klaus H. Maier-Hein Andrey Fedorov
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.