Automatic coronary artery labeling is essential for accurate vascular identification and the diagnosis of coronary disease. The task requires delineating the full vasculature and classifying each segment; however, preserving global topology and local demarcation line precision is difficult due to complex anatomy and blurry contours. We propose a coarse-to-fine ensemble framework with two modules: a Coarse-to-fine Topology Extraction (CTE) network using topology priors for global continuity, and a Progressive Vessel Labeling (PVL) module with multibranch fusion for segmentation and classification. Experiments on the ARCADE dataset achieve a mean F1-score of 0.6028, outperforming state-of-the-art methods and enhancing topological integrity and labeling accuracy. Code: https://github.com/IPMINWU/PGSMODEL.

This paper presents a novel class of cyber security models based on SIR-type formulation. Our effort is on investigating optimal impulse controls arising from a cluster owner under exogenous cyber-attacks. We utilize the SIRS model from epidemiology to represent the spread of cyber-attacks within the cluster and evaluate the impact of protective measures. Within this framework, we determine the optimal defense strategy against effective hacking by formulating and solving a stochastic control problem with optimal switching. By employing dynamic programming principles, we derive a system of quasi-variational inequalities. Due to the inherent nonlinearity and complexity, a closed-form solution is not possible. We use a hybrid deep learning method to approximate the solution by simulating the optimal protection strategies. Finally, the effectiveness of the proposed hybrid deep learning method is validated by comparing it with the deep Galerkin method.

In this paper, we employ numerical methods based on deep learning algorithms for solving controlled stochastic Kolmogorov systems with regime-switching. Different from classical control problems, each component of the state in controlled Kolmogorov systems is nonnegative. Due to the nonlinearity and complexity of the controlled stochastic Kolmogorov systems, we develop a hybrid deep learning method to numerically solve the optimal controls under this system. Subsequently, we apply the hybrid deep learning method to solve a specific case of a controlled stochastic Kolmogorov system, specifically controlled SIS (susceptible-infected-susceptible) systems. Finally, the effectiveness of the proposed hybrid deep learning method is verified through numerical results.