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Cross-Modal Conditioned Reconstruction for Language-Guided Medical Image Segmentation
IEEE Transactions on Medical Imaging
|March 3, 2025
Summary
This study introduces Reconstruction for Language-guided Medical Image Segmentation (RecLMIS), a novel method that improves medical image segmentation by explicitly aligning visual and textual data. RecLMIS enhances accuracy and reduces computational load for better medical AI applications.
Area of Science:
- Artificial Intelligence
- Medical Imaging
- Natural Language Processing
Background:
- Textual information enhances medical image analysis, but language-guided segmentation faces challenges with implicit text embedding.
- Existing methods often produce segmentation results inconsistent with language semantics.
Purpose of the Study:
- To propose a novel cross-modal conditioned Reconstruction for Language-guided Medical Image Segmentation (RecLMIS).
- To explicitly capture cross-modal interactions between medical images and notes for improved segmentation.
Main Methods:
- RecLMIS utilizes a mutual reconstruction approach, assuming aligned medical visual features and notes can reconstruct each other.
- Conditioned interaction adaptively predicts relevant image patches and text words for focused alignment.
- These predicted elements serve as conditioning factors for mutual reconstruction.
Main Results:
- RecLMIS significantly outperforms previous methods, achieving 3.74% higher mIoU on MosMedData+ and 1.89% higher mIoU on QATA-CoV19.
- The method demonstrates a 20.2% reduction in parameter count and a 55.5% decrease in computational load.
- Code is available at https://github.com/ShawnHuang497/RecLMIS.
Conclusions:
- RecLMIS offers a superior approach to language-guided medical image segmentation.
- The explicit cross-modal interaction mechanism effectively aligns visual and textual information.
- The model provides significant improvements in accuracy with reduced computational costs.
