AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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...
Pseudo-labels can exploit unlabeled medical images, but high-overlap masks may still contain local contour errors and selected corrections may disappear during fine-tuning. We recast pseudo-label refinement as a repair-to-model problem and present Boundary-Selective Pseudo-Label Repair and Model Absorption (CPPA). A va...
Few-shot medical image segmentation (FSMIS) seeks to delineate unseen structures from a small support set, but its standard formulation fixes task-defining evidence before inference. This assumption is fragile under acquisition shift, atypical pathology, ambiguous boundaries, and poor image quality. Adding clinician in...
The results demonstrate that the proposed "plug-and-play" evidence-guided framework for reliable medical image segmentation generally achieves a more consistent, spatially coherent uncertainty representation that better aligns with the true error, providing a practical tool for quality control and risk awareness in cli...
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...
Accurate lesion segmentation remains a critical yet challenging task in medical imaging due to scarce pixel-level annotations, inter-observer variability and modality-specific noise. Conventional fully supervised models require exhaustive manual labeling, which limits scalability and clinical applicability. To address...
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