Segmentation is essential for medical image analysis tasks such as intervention planning, therapy guidance, diagnosis, treatment decisions. Deep learning is becoming increasingly prominent for segmentation, where the lack of annotations, however, often becomes the main limitation. Due to privacy concerns and ethical considerations, most medical datasets are created, curated, and allow access only locally. Furthermore, current deep learning methods are often suboptimal in translating anatomical knowledge between different medical imaging modalities. Active learning can be used to select an informed set of image samples to request for manual annotation, in order to best utilize the limited annotation time of clinical experts for optimal outcomes, which we focus on in this work. Our contributions herein are two fold: (1) we enforce domain-representativeness of selected samples using a proposed penalization scheme to maximize information at the network abstraction layer, and (2) we propose a Borda-count based sample querying scheme for selecting samples for segmentation. Comparative experiments with baseline approaches show that the samples queried with our proposed method, where both above contributions are combined, result in significantly improved segmentation performance for this active learning task.
Low-carbon development and environmental remediation are key factors for green resource-based supply chains in China. With this aim in mind, by applying game theory under uncertain market demand, this paper incorporates low-carbon development and environmental remediation into a resource-based supply chain coordination model for decentralized and centralized markets. The results show that a centralized market can lead to improvement in total profit. Furthermore, based on an improved Shapley value method, a theoretical model for the centralized market income distribution mechanism is developed that incorporates three corrective risk factors, ecological investment, and technological level. Finally, a numerical analysis is conducted using a MATLAB simulation to obtain intuitive results, which, in turn, show the validity of incentive income distribution mechanisms for green supply chain development in China.
Considering the correlations of the input indexes and the deficiency of calibrating kernel function parameters when support vector machine (SVM) is applied, a forecasting method based on principal component analysis-genetic algorithm-support vector machine (PCA-GA-SVM) is proposed to improve the precision of bus arrival time prediction. And the No. 232 bus in Shenyang City of China is taken as an example. The traditional SVM and Kalman Filtering model and GA-SVM are also employed to make comparative analysis on the prediction rate, respectively. The result indicates that PCA-GA-SVM obtains more accurate prediction results of bus arrival time prediction.
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