The prediction accuracy of the encoding model based on VGG is found to be significantly better than that of the model based on FCN in most voxels but similar to that of fused features. The comparative analysis demonstrates that the CNN performing the classification task is more similar to human visual processing than that performing the segmentation task.The automatic detection of epilepsy is essentially the classification of EEG signals of seizures and nonseizures, and its purpose is to distinguish the different characteristics of seizure brain electrical signals and normal brain electrical signals. In order to improve the effect of automatic detection, this study proposes a new classification method based on unsupervised multiview clustering results. In addition, considering the high-dimensional characteristics of the original data samples, a deep convolutional neural network (DCNN) is introduced to extract the sample features to obtain deep features. The deep feature reduces the sample dimension and increases the sample separability. The main steps of our proposed novel EEG detection method contain the following three steps first, a multiview FCM clustering algorithm is introduced, and the training samples are used to train the center and weight of each view. Then, the class center and weight of each view obtained by training are used to calculate the view-weighted membership value of the new prediction sample. Finally, the classification label of the new prediction sample is obtained. Experimental results show that the proposed method can effectively detect seizures.Transesophageal echocardiography (TEE) has become an essential tool in interventional cardiologist's daily toolbox which allows a continuous visualization of the movement of the visceral organ without trauma and the observation of the heartbeat in real time, due to the sensor's location at the esophagus directly behind the heart and it becomes useful for navigation during the surgery. However, TEE images provide very limited data on clear anatomically cardiac structures. Instead, computed tomography (CT) images can provide anatomical information of cardiac structures, which can be used as guidance to interpret TEE images. In this paper, we will focus on how to transfer the anatomical information from CT images to TEE images via registration, which is quite challenging but significant to physicians and clinicians due to the extreme morphological deformation and different appearance between CT and TEE images of the same person. In this paper, we proposed a learning-based method to register cardiac CT images to TEE images. In the proposed method, to reduce the deformation between two images, we introduce the Cycle Generative Adversarial Network (CycleGAN) into our method simulating TEE-like images from CT images to reduce their appearance gap. Then, we perform nongrid registration to align TEE-like images with TEE images. The experimental results on both children' and adults' CT and TEE images show that our proposed method outperforms other compared methods. It is quite noted that reducing the appearance gap between CT and TEE images can benefit physicians and clinicians to get the anatomical information of ROIs in TEE images during the cardiac surgical operation.
  The differential diagnosis of subcentimetre lung nodules with a diameter of less than 1 cm has always been one of the problems of imaging doctors and thoracic surgeons. We plan to create a deep learning model for the diagnosis of pulmonary nodules in a simple method.
 
  Image data and pathological diagnosis of patients come from the First Affiliated Hospital of Zhejiang University School of Medicine from October 1, 2016, to October 1, 2019. After data preprocessing and data augmentation, the training set is used to train the model. The test set is used to evaluate the trained model. At the same time, the clinician will also diagnose the test set.
 
  A total of 2,295 images of 496 lung nodules and their corresponding pathological diagnosis were selected as a training set and test set. After data augmentation, the number of training set images reached 12,510 images, including 6,648 malignant nodular images and 5,862 benign nodular images. The area under the 
  -
  curve of the trained model is 0.836 in the classification of malignant and benign nodules. The area under the ROC curve of the trained model is 0.896 (95% CI 78.96%~100.18%), which is higher than that of three doctors. However, the 
  value is not less than 0.05.
 
  With the help of an automatic machine learning system, clinicians can create a deep learning pulmonary nodule pathology classification model without the help of deep learning experts. The diagnostic efficiency of this model is not inferior to that of the clinician.
 With the help of an automatic machine learning system, clinicians can create a deep learning pulmonary nodule pathology classification model without the help of deep learning experts. The diagnostic efficiency of this model is not inferior to that of the clinician.In December 2019, cases of pneumonia were detected in Wuhan, China, which were caused by the highly contagious coronavirus. This study is aimed at comparing the confusion regarding the selection of effective diagnostic methods to make a mutual comparison among existing SARS-CoV-2 diagnostic tests and at determining the most effective one.  https://www.selleckchem.com/products/unc0379.html  Based on available published evidence and clinical practice, diagnostic tests of coronavirus disease (COVID-19) were evaluated by multi-criteria decision-making (MCDM) methods, namely, fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS). Computerized tomography of chest (chest CT), the detection of viral nucleic acid by polymerase chain reaction, cell culture, CoV-19 antigen detection, CoV-19 antibody IgM, CoV-19 antibody IgG, and chest X-ray were evaluated by linguistic fuzzy scale to compare among the diagnostic tests. This scale consists of selected parameters that possessed different weights which were determined by the experts' opinions of the field. The results of our study with both proposed MCDM methods indicated that the most effective diagnosis method of COVID-19 was chest CT. It is interesting to note that the methods that are consistently used in the diagnosis of viral diseases were ranked in second place for the diagnosis of COVID-19. However, each country should use appropriate diagnostic solutions according to its own resources. Our findings also show which diagnostic systems can be used in combination.