Cardiac magnetic resonance imaging (CMR) is crucial for the pathological segmentation of the central muscle in the diagnosis of myocardial infarction (MI) patients. The automatic segmentation technology of medical images has achieved significant success with the development of deep learning technology. However, due to the low contrast of the target area edges, irregular lesion areas, and insufficient medical image data, automatic segmentation of myocardial pathology still has great challenges. In this article, we propose an improved RU-Net model called a Multimodal RU- -Net. Used to segment edema and scar areas in multimodal cardiac CMR data. In this network, we use RU-Net as the basic model, embed our proposed multimodal image feature extraction module (MFF) in the encoding path to enhance the extraction of complementary information between different modal images, and add attention modules in the skip connection and encoding path to enhance attention to the regions of interest in the image. The experiment shows that both modules mentioned above effectively improve segmentation accuracy. In addition, we have adopted methods such as data augmentation, deep supervision, and combination loss to further improve segmentation accuracy. We evaluated multimodal RU-Net on the MyoPS2020 challenge dataset and achieved a Dice score of 64.4% in scar segmentation and 70.7% in edema and scar segmentation. We achieved almost equivalent performance to the most advanced single stage segmentation methods on the MyoPS 2020 ranking, and our proposed method outperformed it in the standard deviation of dice scores, The test results are more stable. This indicates that our proposed method is meaningful for automatic segmentation of myocardial pathology.
In the autonomous driving domain, the fisheye camera is often used as an important sensor of environment perception. In order to realize the simulation and verification of advanced intelligent driving assistance system (ADAS), the simulation of fisheye camera is becoming more and more important. The traditional camera calibration has many problems, such as long process, poor accuracy and complex process. The checkerboard was used as the external parameters calibrating board, which was imported into VTD scene simulation software, and the external parameter calibration of the fisheye camera was realized through the coordinate system transformation. The tool LensDistortion is used to generate intrinsic parameter distortion. The results show that the fisheye camera calibration method can be well applied to validate the function of ADAS domain controller.
Aiming at the fatigue failure problem caused by the cycling influence of electrical stress and thermal stress of IGBT during working state, this paper presents a remaining useful life (RUL) prediction method based on Snake Optimizer (SO) to optimize the stacked bidirectional long short-term memory (Bi-LSTM) neural network model. Firstly, the peak voltage of collector-emitter in the IGBT aging data provided by NASA data center is selected as the characteristic parameter, and the wavelet transformation is used to preprocess the data. Secondly, the Bi-LSTM neural network model is constructed and the attention mechanism is introduced to calculate the allocation weight of the time series, which enhances the expression ability of the nonlinear features of the hidden layer, and adopts SO to optimize the BiALSTM neural network training hyperparameters and construct the SO-Bi-ALSTM neural network prediction model. Finally, the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean squared error (RMSE) are selected as the evaluation criteria, and the prediction accuracy of extreme learning machine (ELM), Transformer, LSTM and SO-Bi-ALSTM models is compared and analyzed. The results show that the MAE predicted by the SO-Bi-ALSTM model is 0.0173, the MAPE is 0.0921%, and the RMSE is 0.0160, which indicates the prediction performance of SO-Bi-ALSTM is better than the other methods.
As the digital wave sweeps the world, digital twin emerge as the times require, which digitally establishes a multi-dimensional dynamic virtual model of a physical entity. At present, there is a broad space for intelligent development in the medical field. Digital twin medicine has become an important development direction of intelligent medicine. The application of digital twin technology in the field of personalized medicine and digital medical system will promote the fundamental change of traditional electronic health and push it into a new era of personalized medicine and digital medical system. This paper investigates the research status of digital twin technology in the medical field and conducts quantitative statistical analysis. The results show that digital twin has a certain basis in medical development and can play a pivotal role in formulating highly personalized treatment plans in the future. In addition, the main concepts of digital twin and some applications in other fields are summarized, and some application examples and visions of digital twin in personalized medicine and digital medical systems are introduced. Finally, other issues and challenges of digital twin in medicine are briefly discussed.
With the development of computer vision technology and smart agriculture, deep learning techniques have been widely applied to crop pest identification tasks. However, existing studies do not consider the problem of large differences in pests across multiple growth stages, leading to unsatisfactory performance in pest identification in practical applications. This article proposes a simple framework for multi-stage prediction of pests, which can effectively predict the growth stage of pests and improve pest classification performance. The framework consists of a classification branch and a stage prediction branch. The stage prediction branch predicts the growth stage of pests based on feature similarity using K-Means, and guides the classification branch to classify images from different stages into different categories to avoid interfering with the performance of the classifier. In addition, to update the entire network parameters, we propose a multi-stage cross-entropy loss that optimizes feature extractors and classifiers by fusing image labels and stage prediction outputs. Experimental results show that the proposed multi-stage prediction framework for pest identification can accurately classify pest stages and improve pest classification accuracy. In addition, our work provides research ideas for pest stage prediction and identification, which is expected to help achieve more efficient pest control.
