Pulmonary nodule detection with low-dose computed tomography (LDCT) is indispensable in early lung cancer screening. Although existing methods have achieved excellent detection sensitivity, nodule detection still faces challenges such as nodule size variation and uneven distribution, as well as excessive nodule-like false positive candidates in the detection results. We propose a novel two-stage nodule detection (TSND) method. In the first stage, a multi-scale feature detection network (MSFD-Net) is designed to generate nodule candidates. This includes a proposed feature extraction network to learn the multi-scale feature representation of candidates. In the second stage, a candidate scoring network (CS-Net) is built to estimate the score of candidate patches to realize false positive reduction (FPR). Finally, we develop an end-to-end nodule computer-aided detection (CAD) system based on the proposed TSND for LDCT scans. Experimental results on the LUNA16 dataset show that our proposed TSND obtained an excellent average sensitivity of 90.59% at seven predefined false positives (FPs) points: 0.125, 0.25, 0.5, 1, 2, 4, and 8 FPs per scan on the FROC curve introduced in LUNA16. Moreover, comparative experiments indicate that our CS-Net can effectively suppress false positives and improve the detection performance of TSND.
Energy storage solutions are strategically important for achieving carbon neutrality and carbon peaking goals. However, high installation costs, demand mismatch, and low equipment utilization have prevented the large-scale commercialization of traditional energy storage. The shared energy storage mode that relies on sharing economy can effectively overcome these problems and has recently attracted widespread attention. In this mini-review, firstly, the concept of shared energy storage is discussed and its application in different countries is illustrated. Second, two core issues in the shared energy storage research—optimal energy scheduling and rational profit distribution—are sorted out and the common modeling approaches and solving algorithms are summarized. Additionally, the dilemma of balancing energy efficiency with distribution fairness faced by the practical application of shared energy storage is pointed out. On this basis, blockchain technology is pointed out to solve the above dilemma of shared energy storage and key directions are given for future research.
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