AIMC Topic: Neural Networks, Computer

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Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation.

Medical image analysis
Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI). However, existing deep learning-based image reconstruction methods typically apply weight-shar...

Esophageal cancer detection based on classification of gastrointestinal CT images using improved Faster RCNN.

Computer methods and programs in biomedicine
PURPOSE: Esophageal cancer is a common malignant tumor in life, which seriously affects human health. In order to reduce the work intensity of doctors and improve detection accuracy, we proposed esophageal cancer detection using deep learning. The ch...

Role of deep learning in brain tumor detection and classification (2015 to 2020): A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
During the last decade, computer vision and machine learning have revolutionized the world in every way possible. Deep Learning is a sub field of machine learning that has shown remarkable results in every field especially biomedical field due to its...

Iterative confidence relabeling with deep ConvNets for organ segmentation with partial labels.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Training deep ConvNets requires large labeled datasets. However, collecting pixel-level labels for medical image segmentation is very expensive and requires a high level of expertise. In addition, most existing segmentation masks provided by clinical...

Comparison of texture-based classification and deep learning for plantar soft tissue histology segmentation.

Computers in biology and medicine
Histomorphological measurements can be used to identify microstructural changes related to disease pathomechanics, in particular, plantar soft tissue changes with diabetes. However, these measurements are time-consuming and susceptible to sampling an...

Assessing the speed-accuracy trade-offs of popular convolutional neural networks for single-crop rib fracture classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Rib fractures are injuries commonly assessed in trauma wards. Deep learning has demonstrated state-of-the-art accuracy for a variety of tasks, including image classification. This paper assesses the speed-accuracy trade-offs and general suitability o...

Exploring the relationship between the dielectric properties and viability of human normal hepatic tissues from 10 Hz to 100 MHz based on grey relational analysis and BP neural network.

Computers in biology and medicine
Liver is an important parenchyma organ, and its tissue viability plays an important role in liver transplantation and liver ischemic injury assessment. Dielectric property is a useful biophysical feature that provides insights into the structure and ...

Non-Contact Respiration Measurement Method Based on RGB Camera Using 1D Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Conventional respiration measurement requires a separate device and/or can cause discomfort, so it is difficult to perform routinely, even for patients with respiratory diseases. The development of contactless respiration measurement technology would...

Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation.

Environmental science and pollution research international
Water is a prime necessity for the survival and sustenance of all living beings. Over the past few years, the water quality of rivers is adversely affected due to harmful wastes and pollutants. This ever-increasing water pollution is a big matter of ...

Learning to recognize while learning to speak: Self-supervision and developing a speaking motor.

Neural networks : the official journal of the International Neural Network Society
Traditionally, learning speech synthesis and speech recognition were investigated as two separate tasks. This separation hinders incremental development for concurrent synthesis and recognition, where partially-learned synthesis and partially-learned...