Prompts can be integrated deep into image segmentation networks through channel-wise attention, rather than just at the end, making models better at finding anatomical structures across different medical imaging modalities.
This paper introduces PCCA, a mechanism that uses text prompts to guide how neural networks process medical images at multiple levels, improving segmentation accuracy across different body parts and imaging types. The method adaptively adjusts which image features matter most based on the prompt, achieving 10-23% improvements over standard approaches.