Medical Image Analysis

Medical image analysis uses computational methods to extract meaningful information from medical images, primarily aiming to improve diagnosis, treatment planning, and disease understanding. Current research heavily emphasizes the development and application of deep learning models, including transformers, U-Nets, and novel architectures like Mamba, alongside techniques like self-explainable AI and efficient fine-tuning for improved accuracy, robustness, and explainability. This field is crucial for advancing healthcare, enabling faster and more accurate diagnoses, personalized treatment strategies, and ultimately improving patient outcomes.