Deep learning has been utilized in knowledge-based radiotherapy planning in which a system trained with a set of clinically approved plans is employed to infer a three-dimensional dose map for a given new patient. However, previous deep methods are primarily limited to simple scenarios, e.g., a fixed planning type or a consistent beam angle configuration. This in fact limits the usability of such approaches and makes them not generalizable over a larger set of clinical scenarios. Herein, we propose a novel conditional generative model, Flexible-Cm GAN, utilizing additional information regarding planning types and various beam geometries. A miss-consistency loss is proposed to deal with the challenge of having a limited set of conditions on the input data, e.g., incomplete training samples. To address the challenges of including clinical preferences, we derive a differentiable shift-dose-volume loss to incorporate the well-known dose-volume histogram constraints. During inference, users can flexibly choose a specific planning type and a set of beam angles to meet the clinical requirements. We conduct experiments on an illustrative face dataset to show the motivation of Flexible-Cm GAN and further validate our model's potential clinical values with two radiotherapy datasets. The results demonstrate the superior performance of the proposed method in a practical heterogeneous radiotherapy planning application compared to existing deep learning-based approaches.

The coronavirus disease 2019 (COVID-19) breaking out in late December 2019 is gradually being controlled in China, but it is still spreading rapidly in many other countries and regions worldwide. It is urgent to conduct prediction research on the development and spread of the epidemic. In this article, a hybrid artificial-intelligence (AI) model is proposed for COVID-19 prediction. First, as traditional epidemic models treat all individuals with coronavirus as having the same infection rate, an improved susceptible-infected (ISI) model is proposed to estimate the variety of the infection rates for analyzing the transmission laws and development trend. Second, considering the effects of prevention and control measures and the increase of the public's prevention awareness, the natural language processing (NLP) module and the long short-term memory (LSTM) network are embedded into the ISI model to build the hybrid AI model for COVID-19 prediction. The experimental results on the epidemic data of several typical provinces and cities in China show that individuals with coronavirus have a higher infection rate within the third to eighth days after they were infected, which is more in line with the actual transmission laws of the epidemic. Moreover, compared with the traditional epidemic models, the proposed hybrid AI model can significantly reduce the errors of the prediction results and obtain the mean absolute percentage errors (MAPEs) with 0.52%, 0.38%, 0.05%, and 0.86% for the next six days in Wuhan, Beijing, Shanghai, and countrywide, respectively.

This paper proposes a road following method in outdoor environments based on dark channel prior and color cues, which integrates the atmospheric transmission estimation, mean shift filtering and graph cuts algorithm together. The main idea is fusing scene depth cue with color information in six-dimensional feature space for road image following. Specifically, the dark channel prior based transmission estimation is firstly employed to recover depth cue. Then the mean shift filtering in the weighted color-depth space is proposed. Finally, graph cuts algorithm is applied to achieve the final road detection. Experimental results indicate the proposed method has excellent performance in complex outdoor environments.

This paper proposes an efficient fusion strategy of monocular depth cue and other image features for natural image segmentation and grouping. The main idea is to improve the performance of image clustering via fusing depth cue, color, spatial location, and edge confidence in six-dimensional color-depth feature space. It integrates the monocular depth cue estimation, mean shift filtering and graph cuts algorithm together. Firstly, the dark channel prior based atmospheric transmission estimation is employed to recover monocular depth cue. Then the mean shift filtering in the weighted color-depth space is proposed to obtain cluster regions with correct boundaries. Finally, graph cuts algorithm is applied to achieve the final regional grouping. Experimental results indicate the proposed method has excellent performance in outdoor natural environments.