AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
MCSD-Net is proposed, a heterogeneous dual-student framework that pairs a structure-oriented Mamba student with a detail-oriented ConvNeXt student and its Cross-Scale Selective Fusion (CS2F) module uses input-dependent gates to combine aligned decoder features and branch logits.
Semi-supervised medical image segmentation methods have drawn wide attention as they reduce reliance on heavily annotated data. However, existing models suffer from confirmation bias with limited annotations, and structural or parameter coupling hinders self-correction, especially for medical images with ambiguous boun...
Although accurate semi-supervised 3D medical image segmentation is vital for clinical diagnostics, existing approaches face two critical bottlenecks. On the one hand, most traditional networks struggle to simultaneously capture global contextual information and preserve fine-grained edge details in their architecture....
DABAL is proposed, a semi-supervised framework designed to improve both supervision reliability and contour localization and introduces a Dynamic-static Domain Adaptive Adapter (DDAA) into the Segment Anything Model (SAM) encoder to preserve stable structural priors while providing input-dependent feature compensation...
Pseudo-labeling is a strong paradigm for semi-supervised medical image segmentation, yet its effectiveness is highly sensitive to confidence thresholding. In abdominal multi-organ segmentation, a fixed global threshold is particularly suboptimal because organ classes differ substantially in size, appearance, and learni...
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