Integrating Statistical Models and Deep Learning for Advanced Medical Image Analysis - Mülle, Johannes; Mateo, Bianchi; Bianchi, Francesca (Journal of Theory and Practice in Engineering and Technology, 2024)
Recent advancements in computer vision and deep learning have profoundly impacted medical image analysis, particularly in tasks such as classification, segmentation, and object detection. Traditional methods reliant on hand-crafted features have given way to deep convolutional neural networks (CNNs), which autonomously learn intricate image representations, enhancing accuracy and efficiency in interpreting medical images (Elyan et al., 2022). Integrating statistical models of shape and appearance with CNN architectures has further bolstered diagnostic capabilities, offering robust frameworks to characterize anatomical structures and variations across diverse patient datasets. Despite these strides, challenges persist in deploying these technologies within clinical settings, including data heterogeneity and model interpretability. This paper critically reviews the integration of statistical models and deep learning in medical image analysis, identifies key challenges, and proposes future research directions to foster the adoption of intelligent computer vision systems in healthcare.