Creating a Medical Image Analysis System with U-Net: A Deep Dive
Introduction to Medical Image Segmentation Medical image segmentation is a crucial task in the field of medical imaging, enabling the precise identification and isolation of specific regions of interest within images. This process is vital for diagnosis, treatment planning, and surgical interventions. One of the most popular and effective architectures for this task is the U-Net, a type of convolutional neural network (CNN) specifically designed for biomedical image segmentation. What is U-Net? U-Net was first introduced in 2015 and has since become a cornerstone in medical image analysis. Its architecture is characterized by a U-shaped structure, which efficiently combines contextual information with localization using skip connections. Here’s a simplified overview of how it works: ...