  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
