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

arXiv: https://arxiv.org/abs/2404.10892 · HTML: https://mlsanity.com/paper/2404.10892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks](https://mlsanity.com/paper/2402.08098/) (2024)

## Abstract (arXiv)

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.