AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The results support further evaluation of ML-Retinex as a compact feature-modulation strategy in the tested CMU-Net/BUSI setting and support further evaluation of ML-Retinex as a compact feature-modulation strategy in the tested CMU-Net/BUSI setting.
Role-labelled data-governance audits are therefore necessary before pooled breast-ultrasound training results are interpreted as evidence of external generalization.
A targeted source-inclusion sensitivity analysis indicates model- and cohort-dependent sensitivity to training-pool composition, rather than a universal benefit from adding data or a universally preferred configuration, in breast ultrasound segmentation.
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