Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study

Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) images.Methods: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ performance.Results: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance.Conclusions: MDCS demonstrated excellent performance in the detection and classification of breast lesions,and greatly enhanced the overall performance of radiologists.

Two-stage capacity optimization approach of multi-energy system considering its optimal operation

With the depletion of fossil fuel and climate change,multi-energy systems have attracted widespread attention in buildings.Multi-energy systems,fuelled by renewable energy,including solar and biomass energy,are gain-ing increasing adoption in commercial buildings.Most of previous capacity design approaches are formulated based upon conventional operating schedules,which result in inappropriate design capacities and ineffective operating schedules of the multi-energy system.Therefore,a two-stage capacity optimization approach is pro-posed for the multi-energy system with its optimal operating schedule taken into consideration.To demonstrate the effectiveness of the proposed capacity optimization approach,it is tested on a renewable energy fuelled multi-energy system in a commercial building.The primary energy devices of the multi-energy system consist of biomass gasification-based power generation unit,heat recovery unit,heat exchanger,absorption chiller,elec-tric chiller,biomass boiler,building integrated photovoltaic and photovoltaic thermal hybrid solar collector.The variable efficiency owing to weather condition and part-load operation is also considered.Genetic algorithm is adopted to determine the optimal design capacity and operating capacity of energy devices for the first-stage and second-stage optimization,respectively.The two optimization stages are interrelated;thus,the optimal design and operation of the multi-energy system can be obtained simultaneously and effectively.With the adoption of the proposed novel capacity optimization approach,there is a 14%reduction of year-round biomass consumption compared to one with the conventional capacity design approach.

Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings

A genetic algorithm-determined deep feedforward neural network architecture(GA-DFNN)is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United Kingdom.Due to the comprehensive relationship between affecting factors and real-world building electricity consumption,the adoption of multiple hidden layers in the deep neural network(DFNN)algorithm would improve its prediction accuracy.The architecture of a DFNN model mainly refers to its quantity of hidden layers,quantity of neurons in the hidden layers,activation function in each layer and learning process to obtain the connecting weights.The optimal architecture of DFNN model was generally determined through a trial-and-error process,which is an exponential combinatorial problem and a tedious task.To address this problem,genetic algorithm(GA)is adopted to automatically design an optimal architecture with improved generalization ability.One year and six months of measurement data from a campus building is used for training and testing the proposed GA-DFNN model,respectively.To demonstrate the effectiveness of the proposed GA-DFNN prediction model,its prediction performance,including mean absolute percentage error,coefficient of determination,root mean square error and mean absolute error,was compared to the reference feedforward neural network models with single hidden layer,DFNN models with other architecture,random search determined DFNN model,long-short-term-memory model and temporal convolutional network model.The comparison results show that the proposed GA-DFNN predictive model has superior performance than all the reference prediction models,demonstrating the optimization effectiveness of GA and the prediction effectiveness of DFNN model with multiple hidden layers and optimal architecture.