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Found 5 papers, 1 papers with code
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images
To guide the segmentation branch to learn from richer high-resolution features, we propose a feature affinity module and a scale affinity module to enhance the multi-task learning of the dual branches.
Asymptotic Optimality of Myopic Ranking and Selection Procedures
In this paper, we theoretically analyze these myopic procedures and prove that they also satisfy the optimality conditions of R&S, just like some other popular R&S methods.
Convergence Rate Analysis for Optimal Computing Budget Allocation Algorithms
It builds the optimality conditions for the number of samples allocated to each design, and the sample allocation that satisfies the optimality conditions is shown to asymptotically maximize the probability of correct selection for the best design.
On the Finite-Time Performance of the Knowledge Gradient Algorithm
Due to the complex calculation of KG, theoretical analysis of this algorithm is difficult, and existing results are mostly about the asymptotic performance of it, e. g., consistency, asymptotic sample allocation, etc.
Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images
The novel coronavirus disease 2019 (COVID-19) characterized by atypical pneumonia has caused millions of deaths worldwide.
