Results 11 to 20 of about 148,198 (270)
CT images-based 3D convolutional neural network to predict early recurrence of solitary hepatocellular carcinoma after radical hepatectomy [PDF]
Diagnostic and Interventional Radiology, 2022 PURPOSEThe high rate of recurrence of hepatocellular carcinoma (HCC) after radical hepatectomy is an important factor that affects the long-term survival of patients.
Hao Cui +5 more
doaj +2 more sources
3D Pose Regression Using Convolutional Neural Networks [PDF]
2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017 3D pose estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding. Most state-of-the-art approaches to 3D pose estimation solve this problem as a pose-classification problem in which the pose space is discretized into bins and a CNN classifier is used to predict a pose bin.
Mahendran, Siddharth +2 more
openaire +2 more sources
Physics-constrained 3D convolutional neural networks for electrodynamics
APL Machine Learning, 2023 We present a physics-constrained neural network (PCNN) approach to solving Maxwell’s equations for the electromagnetic fields of intense relativistic charged particle beams. We create a 3D convolutional PCNN to map time-varying current and charge densities J(r, t) and ρ(r, t) to vector and scalar potentials A(r, t) and φ(r, t) from which we generate ...
Alexander Scheinker, Reeju Pokharel
openaire +3 more sources
Resource Efficient 3D Convolutional Neural Networks [PDF]
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 Recently, convolutional neural networks with 3D kernels (3D CNNs) have been very popular in computer vision community as a result of their superior ability of extracting spatio-temporal features within video frames compared to 2D CNNs. Although there has been great advances recently to build resource efficient 2D CNN architectures considering memory ...
Köpüklü, Okan +3 more
openaire +2 more sources
3D Convolutional Neural Networks for Human Action Recognition [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 We consider the automated recognition of human actions in surveillance videos. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. Convolutional neural networks (CNNs) are a type of deep model that can act directly on the raw inputs. However, such models are currently limited to handling 2D inputs.
Shuiwang Ji, Wei Xu, Ming Yang, Kai Yu
openaire +2 more sources
Asymmetric 3D Convolutional Neural Networks for action recognition [PDF]
Pattern Recognition, 2019 Convolutional Neural Network based action recognition methods have achieved significant improvements in recent years. The 3D convolution extends the 2D convolution from operating on one single frame to a video clip, so it is able to extract effective spatial-temporal features for better analysis of human activities in videos.
Hao Yang +6 more
openaire +1 more source
Alzheimer’s disease Detection: A deep Learning-based Approach
JISR on Computing, 2023 Mental health is an important part of a successful life for a person whether elderly, children, or young. Alzheimer’s is a fatal brain disease that severely damages the human brain, especially in the elderly. One way to prevent Alzheimer's disease is by
Muhammad Wasim
doaj +1 more source
Remote Sensing, 2023 In recent years, convolutional neural networks (CNNs) have been increasingly leveraged for the classification of hyperspectral imagery, displaying notable advancements. To address the issues of insufficient spectral and spatial information extraction and
Yicheng Hu, Shufang Tian, Jia Ge
doaj +1 more source
International Journal of Food Properties, 2021 The hyperspectral image is a three-dimensional (3D) hypercube with spectral and spatial continuity. Traditional hyperspectral imaging (HSI) processing mainly focuses on spectral information.
Qingshuang Mu +5 more
doaj +1 more source
Инновационная медицина Кубани, 2023 Background: Computed tomography angiography (CTA) is the primary and minimally invasive imaging modality currently used for diagnosis and monitoring of intracranial aneurysms as well as preoperative planning of their treatment.
E. I. Zyablova +5 more
doaj +1 more source