Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.
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A mixed-methods case study of Dhamtari's Livelihood College identifies gendered access barriers, manual counseling overload, and underused digital assets as sources of inefficiency in Indian skill development programs.
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Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.
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Lessons from Skill Development Programs -- Livelihood College of Dhamtari
A mixed-methods case study of Dhamtari's Livelihood College identifies gendered access barriers, manual counseling overload, and underused digital assets as sources of inefficiency in Indian skill development programs.