Automatic classification of prostate MR series type using image content and metadata
This work addresses a domain-specific issue for medical imaging researchers and AI practitioners by improving efficiency in data curation for prostate cancer studies.
The authors tackled the problem of incomplete or missing metadata preventing effective automation in classifying prostate MR series types, achieving superior results by combining image data and DICOM metadata compared to using either alone.
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.