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Vector-Quantization-Driven Active Learning for Efficient Multi-modal Medical Segmentation with Cross-Modal Assistance
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AI
DOI：10.1007/978-3-032-04981-0_64.png)
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Abstract
En 中文
Multi-modal medical image segmentation leverages complementary information across different modalities to enhance diagnostic accuracy, but faces two critical challenges: the requirement for extensive paired annotations and the difficulty in capturing complex inter-modality relationships. While Active Learning (AL) can reduce annotation burden through strategic sample selection, conventional methods suffer from unreliable uncertainty quantification. Meanwhile, Vector Quantization (VQ) offers a mechanism for encoding inter-modality relationships, yet existing implementations struggle with codebook misalignment across modalities. To address these limitations, we propose a novel Vector Quantization - Bimodal Entropy-Guided Active Learning (VQ-BEGAL) framework that employs a dual-encoder architecture with VQ to discretize continuous features into distinct codewords, effectively preserving modality-specific information while mitigating feature co-linearity. Unlike conventional AL methods that separate sample selection from model training, our approach integrates feature-level uncertainty estimation from cross-modal discriminator outputs into the training process-strategically allocating samples with different uncertainty characteristics to optimize specific network components, enhancing both feature extraction stability and decoder robustness. Experiments on benchmark datasets demonstrate that our approach achieves state-of-the-art performance while requiring significantly fewer annotations, making it particularly valuable for real-world clinical applications where labeled data is scarce. The code is available at https://github.com/xf-DU/vq-begal.
Keywords:
Multi-modal Medical Image Segmentation
Cross-Modal Assistance
Vector Quantization
Active Learning
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