Results 101 to 110 of about 2,004,297 (387)
ImageNet classification with deep convolutional neural networks
Communications of the ACM, 2012 We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes.
A. Krizhevsky +2 more
semanticscholar +1 more source
Bone‐wise rigid registration of femur, tibia, and fibula for the tracking of temporal changes
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Background Multiple myeloma (MM) induces temporal alterations in bone structure, such as osteolytic bone lesions, which are challenging to identify through manual image interpretation. The large variation in radiologists' assessments, even at expert centers, further complicates diagnosis.
Arttu Ruohola +5 more
wiley +1 more source
Super Resolution for Noisy Images Using Convolutional Neural Networks
Mathematics, 2022 The images in high resolution contain more useful information than the images in low resolution. Thus, high-resolution digital images are preferred over low-resolution images.
Zaid Bin Mushtaq +5 more
doaj +1 more source
Learning to Detect Violent Videos using Convolutional Long Short-Term Memory
, 2017 Developing a technique for the automatic analysis of surveillance videos in order to identify the presence of violence is of broad interest. In this work, we propose a deep neural network for the purpose of recognizing violent videos.
Lanz, Oswald, Sudhakaran, Swathikiran
core +1 more source
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Background This study aims to develop a novel predictive model for determining human papillomavirus (HPV) presence in oropharyngeal cancer using computed tomography (CT). Current image‐based HPV prediction methods are hindered by high computational demands or suboptimal performance.
Junhua Chen +3 more
wiley +1 more source
ILP-M Conv: Optimize Convolution Algorithm for Single-Image Convolution Neural Network Inference on Mobile GPUs [PDF]
arXiv, 2019 Convolution neural networks are widely used for mobile applications. However, GPU convolution algorithms are designed for mini-batch neural network training, the single-image convolution neural network inference algorithm on mobile GPUs is not well-studied.
arxiv
Dense-Sparse Deep Convolutional Neural Networks Training for Image Denoising [PDF]
arXiv, 2021 Recently, deep learning methods such as the convolutional neural networks have gained prominence in the area of image denoising. This is owing to their proven ability to surpass state-of-the-art classical image denoising algorithms such as block-matching and 3D filtering algorithm.
arxiv
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Current radiotherapy practices rely on manual contouring of CT scans, which is time‐consuming, prone to variability, and requires highly trained experts. There is a need for more efficient and consistent contouring methods. This study evaluated the performance of the Varian Ethos AI auto‐contouring tool to assess its potential integration into
Robert N. Finnegan +6 more
wiley +1 more source
IEEE Access, 2019 In order to mine information from medical health data and develop intelligent application-related issues, the multi-modal medical health data feature representation learning related content was studied, and several feature learning models were proposed ...
Weidong Liu +6 more
doaj +1 more source
Turkish Journal of Orthodontics, 2022 Objective: The aim of this study is to develop an artificial intelligence model to detect cephalometric landmark automatically enabling the automatic analysis of cephalometric radiographs which have a very important place in dental practice and is used ...
Mehmet Uğurlu
doaj +1 more source