Biomedical Image Analysis

Biomedical image analysis focuses on developing computational methods to extract meaningful information from medical images, aiding in diagnosis, treatment planning, and research. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and transformers, often adapted into architectures like U-Net and YOLO variants, to perform tasks such as segmentation, object detection, and classification. These advancements are crucial for improving diagnostic accuracy, accelerating research, and ultimately enhancing patient care, although challenges remain in ensuring reproducibility and addressing data limitations.