8. Medical Imaging Based Diagnosis Through Machine Learning and Data Analysis

Machine learning techniques have played an essential role in computer-assisted medical image analysis. In this chapter, we will introduce several of our recent achievements with machine learning methods for feature extraction and representation, classification, dense prediction (segmentation and synthesis), and multi-modality analysis, across the pipeline of computer-assisted diagnosis (CAD). These methods consist of both traditional machine learning techniques and state-of-the-art deep learning based approaches. They were proposed to address pain points in the techniques, for example, similarity metric learning for better classification, 3D and sample-adaptive dense prediction models for segmentation and synthesis, and effective multi-modal imaging data fusion. These methods have been employed in different levels of medical imaging applications, such as medical image synthesis within and across imaging modalities, brain tumor segmentation, and mental disease classification. Common approaches used for related research topics are also briefly reviewed.