A quasi-concavity formulation turns global convexity into local differentiable inequalities on a segmentation mask and its derivatives, yielding a convolutional loss that unifies prior convex shape models.
Towards an ex- tended efficientnet-based u-net framework for joint optic disc and cup segmentation in the fundus image.Biomedical Sig- nal Processing and Control, 85:104906, 2023
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D-Convexity: A Unified Differentiable Convex Shape Prior via Quasi-Concavity for Data-driven Image Segmentation
A quasi-concavity formulation turns global convexity into local differentiable inequalities on a segmentation mask and its derivatives, yielding a convolutional loss that unifies prior convex shape models.