Comparison of Supervised Learning and Unsupervised Learning in the Diagnosis of Medical Imaging Diseases

Medical image registration represents a pivotal element in the field of disease image analysis, acting as the essential precursor for a multitude of sophisticated analytical tasks. In recent years, traditional methodologies have encountered significant challenges in meeting the evolving demands of clinical practice. In contrast, deep learning-based strategies have emerged as powerful alternatives, showcasing their ability to facilitate more rapid and accurate registration processes, thereby exerting a profound impact on clinical applications. Within the specialized domain of medical imaging, the intricate level of expertise required for domain knowledge imposes rigorous standards on annotators, which in turn leads to increased annotation costs. As a result, the efficacy of supervised learning approaches compared to unsupervised learning methodologies can exhibit substantial variability in real-world applications. This paper systematically utilizes a diverse array of medical imaging datasets to rigorously assess the performance outcomes of both supervised and unsupervised learning techniques, specifically in relation to their practical applications in the medical imaging landscape.