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10 hours ago · Nevertheless, operating CNNs in radiology is fraught with challenges, such as limited datasets and overfitting, demanding the invention of new methodologies to tune CNN structures. Researchers are proposing new optimization methods from neural pruning to knowledge distillation, to raise the performance of CNNs in medical imaging.
3 days ago · Therefore, CNNs can perform image processing tasks with better efficiency. Convolutional neural networks utilize the parameter-sharing technique for efficiency in management of image data. The convolutional layers work with the same filter for scanning the complete image, thereby reducing the number of parameters.
2 days ago · Convolutional neural networks, or CNNs, are a significant advancement in processing spatially related data, such as photographs. At higher resolutions, the convolutional layers recognize edges, colors, and textures in the input images; at lower resolutions, they abstract the characteristics.
4 days ago · The goal of time series imputation is to recover the value of missing observations precisely. To describe the data missingness of time series , we denote ∈{0,1} × as the mask matrix, where ,
3 days ago · Figure 1: Qualification of a CNN classification using a reliably executed qualifying block. - "Hybrid Convolutional Neural Networks with Reliability Guarantee"
3 days ago · Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in many computer vision tasks, such as classification, segmentation, and registration [37, 38]. However, due to the loss of information in the continuous down-sampling layers of CNNs, they perform poorly in identifying precise object boundaries.
3 days ago · In addition, CNNs have preceded a radical change in treatment planning through the information-derived and data-driven personalized approaches implementation. A joint operation of patient data and medical images with the CNNs is a great help for a doctor’s purpose of designing a treatment plan that is beneficial to specific patients.
