Convolutional Neural Networks (CNNs) have become widely adopted for medical
image segmentation tasks, demonstrating promising performance. However, the
inherent inductive biases in convolutional architectures limit their ability to
model long-range dependencies and spatial correlations. While recent
transformer-based architectures address these limitations by leveraging
self-attention mechanisms to encode long-range dependencies and learn
expressive representations, they often struggle to extract low-level features
and are highly dependent on data availability. This motivated us for the
development of GLIMS, a data-efficient attention-guided hybrid volumetric
segmentation network. GLIMS utilizes Dilated Feature Aggregator Convolutional
Blocks (DACB) to capture local-global feature correlations efficiently.
Furthermore, the incorporated Swin Transformer-based bottleneck bridges the
local and global features to improve the robustness of the model. Additionally,
GLIMS employs an attention-guided segmentation approach through Channel and
Spatial-Wise Attention Blocks (CSAB) to localize expressive features for
fine-grained border segmentation. Quantitative and qualitative results on
glioblastoma and multi-organ CT segmentation tasks demonstrate GLIMS'
effectiveness in terms of complexity and accuracy. GLIMS demonstrated
outstanding performance on BraTS2021 and BTCV datasets, surpassing the
performance of Swin UNETR. Notably, GLIMS achieved this high performance with a
significantly reduced number of trainable parameters. Specifically, GLIMS has
47.16M trainable parameters and 72.30G FLOPs, while Swin UNETR has 61.98M
trainable parameters and 394.84G FLOPs. The code is publicly available on
this https URL

GLIMS 是一种数据效率高、注意力引导的混合体积分割网络，利用了 Dilated Feature Aggregator Convolutional Blocks (DACB)，并结合了 Swin Transformer-based bottleneck，通过 Channel 和 Spatial-Wise Attention Blocks (CSAB) 实现了注意力引导分割方法，证明了其在复杂性和准确性方面的出色表现。