SparseQ: Jensen-Shannon Divergence Guided Query Sparsified-Attention with Convolutional Distilling for Medical Image Segmentation

Le résumé fourni par la source

OBJECTIVE: Accurate medical image segmentation is essential for clinical diagnosis and treatment planning. Although Convolutional Neural Networks (CNNs) excel at capturing local spatial details, they struggle to model long-range dependencies across anatomical regions, which is critical for lesion and tumor delineation. Transformer-based models address this through self-attention but incur prohibitive computational costs that scale quadratically with the number of tokens, limiting deployment in resource-constrained environments. We propose SparseQ, a hybrid framework that combines efficient sparse attention with convolutional feature refinement. The core innovation introduces a Jensen-Shannon Divergence (JSD)-motivated sparsity criterion, efficiently approximated via logit-dispersion scoring, to selectively compute dominant query-key interactions, reducing attention complexity from $\mathcal {O}(L^{2})$ to $\mathcal {O}(L \ln L)$ while preserving segmentation accuracy. A complementary convolutional distilling module with progressively dilated convolutions expands the receptive field without parameter inflation, enabling multi-scale feature learning. The decoder integrates unified content queries with high-resolution pixel embeddings for precise boundary delineation. Extensive experiments on BraTS2023 and BraTS2019 (multi-region tumor segmentation) and ISLES-2022 (binary ischemic lesion segmentation) demonstrate that SparseQ consistently outperforms strong CNN, Transformer, and hybrid baselines, achieving a mean Dice of 85.8% and a mean HD95 of 3.28, while reducing training time and GPU memory. These results validate that principled, content-adaptive sparsity can enable accurate and efficient medical image segmentation suitable for practical use.

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