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Research questionHow can high-resolution medical image segmentation fuse modalities and clinical text without dense cross-attention costs?Medical images and clinical reports contain complementary spatial, functional, and semantic information. Dense cross-modal interactions become expensive on high-resolution, especially volumetric, feature maps while subtle anatomical details must remain usable.
Computer Vision
Health
Image & Video Processing
Machine Learning
Multimodal Models
Latest papersRecent research connected to this question, newest first.CoMLP: Cooperatively-Gated MLPs for Fine-Grained Cross-Modal Information Fusion in Medical Image SegmentationThe source studies a cooperatively gated MLP architecture for inter-image and vision-language fusion in medical segmentation. Evidence spans five benchmarks involving 2D and 3D images, multiple imaging modalities, clinical reports, and diverse anatomical regions; it does not establish applicability beyond these evaluated settings.research paper · Sep 4, 2026
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