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  1. 8541
    “…The image-based RA and DL models were constructed by adding kinetic parameters or molecular information to image-only linear discriminant analysis (LDA) and convolutional neural network (CNN) models. The predictive performances of developed models were assessed by receiver operating characteristic (ROC) curve analysis and compared with the DeLong method. …”
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    Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
  2. 8542
    “…We developed a deep learning model based on a deep convolutional neural network (CNN) in the examination. To generalize the applicability of the deep learning model in clinical practice, we compared the deep model with 10 endoscopists and applied it in 3 hospitals across China. …”
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    Development and Validation of a Deep Neural Network for Accurate Identification of Endoscopic Images From Patients With Ulcerative Colitis and Crohn's Disease
  3. 8543
    “…To mitigate theses drawbacks in SDC, we therefore, propose a hierarchical dense dilated deep pyramid feature extraction through convolution neural network (CNN) for single image crowd counting (HDPF). It comprises of three modules: general feature extraction module (GFEM), deep pyramid feature extraction module (PFEM) and fusion module (FM). …”
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    An effective modular approach for crowd counting in an image using convolutional neural networks
  4. 8544
    “…In this study, we developed a state‐of‐the‐art deep learning convolutional neural network (CNN) for image‐to‐image translation across three standards MRI contrasts for the brain. …”
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    Deep learning‐based convolutional neural network for intramodality brain MRI synthesis
  5. 8545
    por Chen, Xiao, Cheng, Jianlin
    Publicado 2022
    “…RESULT: We developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. …”
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    DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network
  6. 8546
    por Yin, Ping, Wang, Hongli
    Publicado 2022
    “…All patients underwent ultrasonography, the convolution neural network (CNN) algorithm was used for image denoising and edge feature extraction, and the performance of the algorithm was evaluated by the Dice coefficient, positive predictive value, sensitivity, and Hausdorff distance. …”
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    Evaluation of Nursing Effect of Pelvic Floor Rehabilitation Training on Pelvic Organ Prolapse in Postpartum Pregnant Women under Ultrasound Imaging with Artificial Intelligence Algorithm
  7. 8547
    por Li, Li, Sun, Nan
    Publicado 2022
    “…This bidirectional ConvLSTM extracts the time-domain features of EEG signals and integrates the feature extraction capability of the CNN and the sequence processing capability of LSTM. …”
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    Attention-Based DSC-ConvLSTM for Multiclass Motor Imagery Classification
  8. 8548
    “…METHODS: We propose a regression convolutional neural network (CNN) using image features to estimate the six-dimensional pose of arbitrarily oriented US planes relative to the fetal brain centre. …”
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    Deep learning-based plane pose regression in obstetric ultrasound
  9. 8549
    “…The model first fuses protein evolutionary information and drug structure information, then a deep learning convolutional neural network (CNN) to mine its hidden features, and finally accurately predicts the associated DTIs by extreme learning machine (ELM). …”
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    MSPEDTI: Prediction of Drug–Target Interactions via Molecular Structure with Protein Evolutionary Information
  10. 8550
    “…Furthermore, a trailblazing custom CNN-based model, CoviXNet, has been proposed consisting of 15 layers that take efficiency into account. …”
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    CoviXNet: A novel and efficient deep learning model for detection of COVID-19 using chest X-Ray images
  11. 8551
    “…In the third step, we extract features of the upscaled version of the image using a residual connection-based mini-CNN and again upscale the feature map by a factor of two. …”
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    A new generative adversarial network for medical images super resolution
  12. 8552
    “…References have shown that deep convolutional neural networks (CNN) have a high sensitivity in CXR readings. In this retrospective study, we collected the CXR images of patients over 65 hospitalized with pneumonia or pulmonary edema diagnosis between 2016 and 2020. …”
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    Using Artificial Intelligence to Establish Chest X-Ray Image Recognition Model to Assist Crucial Diagnosis in Elder Patients With Dyspnea
  13. 8553
    “…The data set as well as the code for the CNN are publicly available: https://github.com/BorgwardtLab/covid19_ultrasound. …”
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    ACCELERATING COVID-19 DIFFERENTIAL DIAGNOSISWITH EXPLAINABLE ULTRASOUND IMAGE ANALYSIS: AN AI TOOL
  14. 8554
    “…METHODS: Convolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. …”
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    Deep learning models for forecasting dengue fever based on climate data in Vietnam
  15. 8555
    “…Compared with some state-of-the-art models (Faster R-CNN, YOLOv3, and YOLOv5), our model shows a low false detection and missing detection rates. …”
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    Intelligent Monitoring System of Migratory Pests Based on Searchlight Trap and Machine Vision
  16. 8556
    “…These feature sets are given to convolutional neural network (CNN) models for training and the trained model can predict the signs from the test inputs. …”
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    Non-contact measurement system for COVID-19 vital signs to aid mass screening—An alternate approach
  17. 8557
    “…We evaluated the algorithms of each component and the CA system as a whole. RESULTS: CNN is the best question classifier with an F1 score of 0.81, and CRF is the best named entity recognizer with an F1 score of 0.87. …”
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    A conversational agent system for dietary supplements use
  18. 8558
    “…All DL models were trained using a deep convolutional neural network (CNN) based on 45 degree CFPs covering the fovea and the optic disk. …”
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    Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients With Branch Retinal Vein Occlusion
  19. 8559
    “…Three missense mutations (rs76740888, rs149664918, and rs78251590) in two genes (PRSS3 and CNN2) of immune-related genes showed significantly different mutation frequencies between the disease group and healthy controls. …”
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    Exome Sequencing Reveals Genetic Variability and Identifies Chronic Prognostic Loci in Chinese Sarcoidosis Patients
  20. 8560
    “…METHODS: In order to obtain denoised X‐ray fluoroscopy images whilst preserving details, we propose a novel deep‐learning‐based denoising framework, namely edge‐enhancement densenet (EEDN), in which an attention‐awareness edge‐enhancement module is designed to increase edge sharpness. In this framework, a CNN‐based denoiser is first used to generate an initial denoising result. …”
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    Edge‐enhancement densenet for X‐ray fluoroscopy image denoising in cardiac electrophysiology procedures
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