Medical Image Processing

Medical image processing focuses on developing computational methods to analyze and interpret medical images, primarily aiming to improve diagnostic accuracy and efficiency. Current research emphasizes the application of deep learning, particularly convolutional neural networks (CNNs) like U-Net and its variants, along with novel architectures such as Inception modules and the integration of foundation models like SAM, to achieve robust segmentation and classification of various medical conditions. These advancements are crucial for improving disease detection, facilitating faster diagnosis, and ultimately enhancing patient care, particularly in areas with limited access to expert radiologists. Furthermore, research addresses challenges like data scarcity through techniques such as self-supervised learning and synthetic data generation.