AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
WFDViM is proposed, a wavelet frequency-decoupled Vision Mamba framework for weak-boundary medical image segmentation that achieves the lowest mean HD95 and ASSD across all four datasets while requiring 13.06 M parameters and 3.05 GFLOPs.
UKDW, a lightweight medical image segmentation framework that jointly improves input quality and cross-layer feature interaction under a unified architecture and training protocol, is proposed and experimental results suggest that, in lightweight medical image segmentation, jointly addressing input degradation and feat...
MGA-UNet, a frequency-aware multi-scale encoder–decoder segmentation framework that integrates wavelet-based frequency decomposition with Mamba-based long-range dependency modelling, demonstrates that frequency-domain decomposition and state-space modelling can complement each other for accurate medical image segmentat...
Accurate segmentation of organs and lesions in medical images is essential for clinical applications such as diagnosis, prognosis, and treatment planning. Vision transformer (ViT)-based models have demonstrated strong performance in medical image segmentation by modeling long-range dependencies through self-attention m...
TvaraNet is pro-posed, an extremely lightweight segmentation network designed to preserve boundary fidelity under strict efficiency constraints and achieves competitive or superior boundary-aware performance compared to heavier architectures.
This study introduces a novel 3D registration framework centered on a dynamic wavelet transform module that achieves superior registration fidelity, highlighting its potential for practical clinical implementation.
RV-Mixer is proposed, a lightweight module for efficient global representation learning with medical-domain priors that achieves competitive or superior performance compared with Transformer, MLP, and Mamba-based approaches while maintaining fewer parameters and lower computational complexity.
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