# Prediction of Geometric Transformation on Cardiac MRI via Convolutional Neural Network

arXiv: https://arxiv.org/abs/2211.06641 · HTML: https://mlsanity.com/paper/2211.06641/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 12 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Visual Chirality](https://mlsanity.com/paper/2006.09512/) (2020)
- [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 the field of medical image, deep convolutional neural networks(ConvNets) have achieved great success in the classification, segmentation, and registration tasks thanks to their unparalleled capacity to learn image features. However, these tasks often require large amounts of manually annotated data and are labor-intensive. Therefore, it is of significant importance for us to study unsupervised semantic feature learning tasks. In our work, we propose to learn features in medical images by training ConvNets to recognize the geometric transformation applied to images and present a simple self-supervised task that can easily predict the geometric transformation. We precisely define a set of geometric transformations in mathematical terms and generalize this model to 3D, taking into account the distinction between spatial and time dimensions. We evaluated our self-supervised method on CMR images of different modalities (bSSFP, T2, LGE) and achieved accuracies of 96.4%, 97.5%, and 96.4%, respectively. The code and models of our paper will be published on: https://github.com/gaoxin492/Geometric_Transformation_CMR