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...
The evidence supports a bounded, abstaining repair policy whose local corrections can be absorbed into one deployable network.
This work reframe FSMIS as a three-layer sequential decision problem, state six hypotheses with an explicit dependency order, and proposes a minimal pilot that can falsify the foundational self-assessment claim before a clinician study.
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...
A hybrid weakly supervised framework that integrates the Segment Anything Model with an Explainable Vision Transformer for robust and interpretable lesion segmentation and delivers noise-robust segmentation, clinically transparent predictions, and efficient label utilization under imperfect supervision is proposed.
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