Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

Abstract

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) of breast tissue are discussed. The algorithms are based on recent advances in multi-dimensional signal processing and aim to advance current state-of-the-art computer-aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi-parametric computer-aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi-supervised deep learning and self-supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high-dimensional medical imaging analysis platform that is based on multi-task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE-MRI. Since some of the approaches discussed are also based on time-lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis.

U-Net-Based Medical Image Segmentation

“…First, medical image processing requires extremely high accuracy for disease diagnosis [18][19][20][21][22][23]. Segmentation in medical imaging refers to pixel-level or voxel-level segmentation.…”

Focal Boundary Dice: Improved Breast Tumor Segmentation from MRI Scan

“…The current study is also subject to some limitations, such as the way to select target regions based on contextual information and weights, to capture the spatial and channel correlations among features [39][40][41], to strengthen information exchange between the spatial and channel features, and to enhance the original features of small targets [42,43] to improve the classification performance of imaged tumors. Especially, further exploration should be conducted on the way to achieve the extraction of boundary features by using the local density deviation of adjacent targets as a reference item.…”

MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method

“…Taking DCE-MRI as an example, it is reported that DCE-MRIs can be divided into several different representative feature regions according to a certain threshold, and texture features are calculated in these regions to measure the heterogeneity degree of these regions [ 77 , 81 , 82 ]. An increasing number of sophisticated methods cluster similar dynamic enhancement patterns [ 13 , 83 , 84 ] and describe the heterogeneity of vascular characteristics in tumor spaces through DCE-MRIs. But technically, the current stage of tumor heterogeneity analysis is still in its infancy.…”