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Automatic classification of prostate MR series type using image content and metadata
April 18, 2024, 4:44 a.m. | Deepa Krishnaswamy, B\'alint Kov\'acs, Stefan Denner, Steve Pieper, David Clunie, Christopher P. Bridge, Tina Kapur, Klaus H. Maier-Hein, Andrey Fedor
cs.CV updates on arXiv.org arxiv.org
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 …
arxiv classification cs.cv eess.iv image metadata series type
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