Prediction of Geometric Transformation on Cardiac MRI via Convolutional Neural Network
This addresses the labor-intensive annotation issue in medical imaging, though it is incremental as it adapts existing self-supervised ideas to a specific domain.
The paper tackled the problem of requiring large annotated datasets for medical image tasks by proposing a self-supervised method to learn features by predicting geometric transformations on cardiac MRI, achieving accuracies of 96.4%, 97.5%, and 96.4% on different modalities.
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