## 1 Introduction
Furthermore, due to the gradients in the main magnetic field or due to patient motion, the results can show large variations in tumour detail and the image segmentation and classification effects are not ideal. In addition, in traditional high-dimensional DCE-MRI analysis, time domain signals and spatial domain characteristics in the image are processed separately to make a final diagnosis. Such approach results in low specificity of lesion detection, i.e., in a range of 40-80% [5; 6]. The full use of spatial-position and temporal-association information of medical images is essential to obtain high-quality tumour segmentation and classification. Although in this paper we focus on data fusion of such information only within the context of DCE-MRI, the approach is generic to any tumour classification framework. By discussing advances in learning algorithms and classifiers, a new generic approach for multi-dimensional image analysis is proposed. This should also be equally applicable to other medical imaging modalities, such as X-ray tomography, terahertz imaging, acoustic imaging, etc. and has the potential to effectively solve several practical problems encountered in clinical medicine. To clarify these concepts, this paper is structured as follows: Section 1 provides an introduction to breast DCE-MRI as this is the more well-established modality for building breast MR imaging systems. Section 2 discusses generic computer aided detection, diagnosis and classification methodologies applicable to high dimensional (breast tissue) MRI. The importance of breast parenchymal background classification in the identification of breast cancer is highlighted. A discussion on MRI radiomics is also provided as a high-throughput quantitative feature extraction strategy for tumour detection. In addition, computer aided detection and diagnosis methodologies are summarised, and the importance of self-supervised and semi-supervised deep learning methodologies is articulated. These are emergent topics within the current computer science community. The analysis of high-dimensional image datasets is also discussed and placed in the context of recent advances in tensor based image reconstruction. Because artefacts can be generated in image reconstruction as well as in segmentation processes based on multi-channel intensity gradients tensorial de-noising approaches need to be considered. These make use of higher order singular value decomposition routines. However, due to the universal applicability of these algorithms across all biomedical imaging modalities, a more focused discussion regarding their implementation is provided. In Section 3, recent advances in classification algorithms are considered. An emphasis is placed on multi-dimensional time-space enhancement using a deep learning network. Tensorial analysis may also be combined with geometric algebra approaches to create a rich classifier feature input space, so as to improve the training basis and increase the precision of the classifier. Recent advances in self-supervised learning and generative adversarial networks can be incorporated in this framework. The multichannel approach also integrates information acquired from multiple images at different time stamps, so models of predictive value on disease proliferation can be generated. Self-supervised learning allows boosting the performance of machine learning models by using both un-labelled as well as labelled data. Generative adversarial networks enable the discrimination and even sparse data based image regeneration of MRI, thus enabling the adoption of semi-supervised classification strategies. Therefore, we provide an overview of these approaches. The work finally proposes that there are benefits in combining generative adversarial networks with multi-task deep learning approaches. A new unified framework that incorporates multi-dimensional data and multi-task learning is proposed as a way forward for advancing computer aided diagnosis of breast DCE-MRI. Then, conclusions are provided in Section 4.