AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The Dual-Statistical Context Modulation Block (DCM-Block), which integrates Global Average Pooling and Global Max Pooling to generate content-aware dynamic weights, thereby achieving dynamic multi-scale feature fusion at the bottleneck layer is proposed.
SegRWKV, a hybrid architecture that integrates pretrained Vision-RWKV modules in the encoder for efficient global dependency modeling, and a PixelRefinement module in the decoder to improve feature reconstruction and multi-scale alignment, is proposed, highlighting SegRWKV’s superior ability to balance accuracy, effici...
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.
Experiments demonstrate that the proposed cross-layer semantic alignment mechanism outperforms mainstream approaches in terms of Dice, HD95, and Intersection over Union metrics, validating the effectiveness of the cross-layer semantic alignment mechanism for complex medical image segmentation tasks.
GLNet adopts a dual-branch encoder that combines a CNN-based Local Detail Perception Branch with a Mamba-based Global Context Modeling Branch, enabling the joint extraction of fine-grained local features and long-range semantic representations.
Results indicate that SEED-Net can effectively preserve fine structural details, reduce background interference, and produce accurate lesion boundaries, showing its potential for reliable MIS.
Medical image segmentation is a key component of computer-aided diagnosis and treatment planning. Despite substantial progress in deep learning–based models, most existing approaches depend heavily on large annotated datasets and often fail to generalize across heterogeneous clinical environments, limiting their deploy...
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