Automatic classification of prostate MR series type using image content and metadata

• URL: http://arxiv.org/abs/2404.10892v1
• Date: Tue, 16 Apr 2024 20:30:16 GMT
• Title: Automatic classification of prostate MR series type using image content and metadata
• Authors: Deepa Krishnaswamy, Bálint Kovács, Stefan Denner, Steve Pieper, David Clunie, Christopher P. Bridge, Tina Kapur, Klaus H. Maier-Hein, Andrey Fedorov,
• Abstract summary: We 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.
• Score: 1.0959281779554237
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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