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Universal medical image segmentation via in-context cross-attention
Costin F Ciusdel1, Alex Serban1, Tiziano Passerini2
1Foundational Technologies, Siemens SRL, Brasov, Romania.
Frontiers in Artificial Intelligence
|December 11, 2025
Summary
This study introduces a universal medical image segmentation method using cross-attention to select relevant support data, improving accuracy and explainability across diverse datasets and modalities.
Area of Science:
- Medical Image Analysis
- Computer Vision
- Artificial Intelligence
Background:
- Traditional specialist models struggle with adaptation to new medical imaging tasks and distribution shifts.
- Generalist pre-trained models and universal segmentation methods offer solutions, with universal approaches excelling in versatility and sample efficiency.
- Existing universal methods can be improved by incorporating relevant region pre-selection from support sets.
Purpose of the Study:
- To develop a novel universal segmentation method that enhances accuracy by leveraging relevant information from support sets.
- To improve the sample efficiency and integration ease of universal segmentation models in medical image annotation pipelines.
- To provide an explainable AI approach for medical image segmentation.
Main Methods:
- Implemented a universal segmentation approach utilizing cross-attention between query and support images.
- Introduced an attention up-scaling mechanism for efficient cross-attention computation on multi-resolution features.
- Developed an explainability module to visualize relevant support set regions for segmentation outputs.
Main Results:
- Achieved consistent performance improvements across 29 medical datasets, 9 imaging modalities, and 135 segmentation tasks, even with lightweight models.
- Demonstrated proportional gains in segmentation accuracy with increasing support set size, highlighting effective support image selection via cross-attention.
- The explainability module showed competitive or superior interpretability compared to established methods like LayerCAM.
Conclusions:
- The proposed universal segmentation method effectively improves accuracy and explainability in medical image analysis.
- Cross-attention and attention up-scaling are key innovations for versatile and efficient medical image segmentation.
- The approach offers a promising direction for robust and interpretable AI in healthcare imaging.
