# Recognition of Cardiac MRI Orientation via Deep Neural Networks and a Method to Improve Prediction Accuracy

arXiv: https://arxiv.org/abs/2211.07088 · HTML: https://mlsanity.com/paper/2211.07088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 4 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks](https://mlsanity.com/paper/2011.08761/) (2020)

## Abstract (arXiv)

In most medical image processing tasks, the orientation of an image would affect computing result. However, manually reorienting images wastes time and effort. In this paper, we study the problem of recognizing orientation in cardiac MRI and using deep neural network to solve this problem. For multiple sequences and modalities of MRI, we propose a transfer learning strategy, which adapts our proposed model from a single modality to multiple modalities. We also propose a prediction method that uses voting. The results shows that deep neural network is an effective way in recognition of cardiac MRI orientation and the voting prediction method could improve accuracy.