## 3 High-Dimensional Medical Imaging Analysis Based on Deep Learning
### Semi-Supervised Deep Learning Strategies for MRI
#### 3.2.1 Self-Supervised Learning  
For breast tumour MRI samples where for most of the time there is relatively limited annotation, a self-supervised deep learning strategy offers additional possibilities that are worth exploring in an AI context. A self-supervised machine learning approach avoids the use of prior information, which makes it very versatile from a learning perspective and able to cope with different tissue types of medical images that may have never been presented to the classifier. It also returns tissue probabilities for each voxel, which is crucial for a good characterisation of the evolution of lesions. The general idea of self-supervised learning is as follows. A classifier is first trained on the labelled samples. The trained classifier then classifies the unlabelled samples. These samples, or a subset of these samples, are then added to the training set. This process is repeated multiple times until the classifier achieves a particular level of precision.  
In many scenarios, the dataset in question consists of more unlabelled images than labelled ones. Therefore, boosting the performance of machine learning models by using unlabelled as well as labelled data is an important but challenging problem.  
Though self-supervised learning presents one possible solution to the key challenge of identifying a suitable self supervision task, i.e., generating input and output instance pairs from data, existing self-supervised learning strategies applicable to medical images so far have resulted in limited performance improvement. A promising future direction is discussed in the work by [PERSON] et al. [93], who proposed a novel self-supervised learning strategy for medical imaging. The approach focuses on context restoration as a self-supervision task. Specifically, given an image, two small patches are randomly selected and swapped. By repeating this operation a number of times a new image is generated where the intensity distribution is preserved but its spatial information is altered. A CNN can then be trained on the artificially generated image to restore it back to its original version. CNN features identified in the self-supervision learning process can then be used to perform different types of transformations with greater precision, thus improving classification, localisation and segmentation.  
A similar idea is discussed in the work by [PERSON] et al. [94], who proposed a Rubik's cube type recovery as a novel self-supervision task to pre-train 3D neural networks. The proxy task involves two operations, i.e., cube rearrangement and cube rotation, which enforce networks to learn translational and rotational invariant features from raw 3D data. Compared to the train-from-scratch strategy, fine-tuning from the pre-trained network leads to a better accuracy on various tasks, e.g., brain hemorrhage classification and brain tumour segmentation. The self-supervised learning approach can substantially boost the accuracy of 3D deep learning networks on the volumetric medical datasets without using extra data. This approach could also be adapted for MRI voxel datasets and is worth exploring.