Sam2Rad architecture The Sam2Rad architecture incorporates a (hierarchical) two-way attention module to predict prompts for queried objects. Each object/class is represented by learnable queries . The Prompt Predictor Network (PPN) predicts bounding box coordinates of the target object , an intermediate mask prompt , and high-dimensional prompt embeddings . The prompt embeddings can represent various prompts suitable for the task, such as several point prompts or high-level semantic information. The predicted prompts (i.e., , , & ) are then fed to SAM’s mask decoder to generate the final segmentation mask. PPN also supports multi-class medical image segmentation by using class-specific queries .

Sam2Rad: Revolutionizing Medical Image Segmentation with AI-Powered Automation

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Medical imaging has long been a cornerstone of modern healthcare, enabling clinicians to diagnose, treat, and monitor a wide range of conditions. However, the manual segmentation of structures in medical images remains a time-consuming and expertise-intensive task. With the advent of deep learning and foundation models like the Segment Anything Model (SAM), there is growing […]

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