1,858 results match your criteria networks cnns

Convolutional neural networks to identify malformations of cortical development: A feasibility study.

Seizure 2021 May 31;91:81-90. Epub 2021 May 31.

Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA USA.

Objective: To develop and test a deep learning model to automatically detect malformations of cortical development (MCD).

Methods: We trained a deep learning model to distinguish between diffuse cortical malformation (CM), periventricular nodular heterotopia (PVNH), and normal magnetic resonance imaging (MRI). We trained 4 different convolutional neural network (CNN) architectures. Read More

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3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks.

J Zhejiang Univ Sci B 2021 Jun;22(6):462-475

School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

To overcome the computational burden of processing three-dimensional (3D) medical scans and the lack of spatial information in two-dimensional (2D) medical scans, a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2D convolutional neural networks (2D-CNNs). In order to combine the low-level features and high-level features, we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process. Further, in order to resolve the problems of the blurred boundary of the glioma edema area, we superimposed and fused the T2-weighted fluid-attenuated inversion recovery (FLAIR) modal image and the T2-weighted (T2) modal image to enhance the edema section. Read More

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Capsule networks for segmentation of small intravascular ultrasound image datasets.

Int J Comput Assist Radiol Surg 2021 Jun 14. Epub 2021 Jun 14.

Hamburg University of Technology, Institute of Medical Technology and Intelligent Systems, Hamburg, Germany.

Purpose: Intravascular ultrasound (IVUS) imaging is crucial for planning and performing percutaneous coronary interventions. Automatic segmentation of lumen and vessel wall in IVUS images can thus help streamlining the clinical workflow. State-of-the-art results in image segmentation are achieved with data-driven methods like convolutional neural networks (CNNs). Read More

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CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.

Omneya Attallah

Front Neuroinform 2021 28;15:663592. Epub 2021 May 28.

Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt.

Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. Read More

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A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections.

Int J Comput Assist Radiol Surg 2021 Jun 13. Epub 2021 Jun 13.

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Purpose: Accurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-the-art image segmentation technique, but require large annotated datasets for training. Annotation of 3D medical images is time-consuming, requires highly trained raters and may suffer from high inter-rater variability. Read More

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Deep sequence modelling for Alzheimer's disease detection using MRI.

Comput Biol Med 2021 Jun 1;134:104537. Epub 2021 Jun 1.

School of Electrical Engineering and Computing, The University of Newcastle, NSW 2308, Australia.

Background: Alzheimer's disease (AD) is one of the deadliest diseases in developed countries. Treatments following early AD detection can significantly delay institutionalisation and extend patients' independence. There has been a growing focus on early AD detection using artificial intelligence. Read More

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Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural networks.

Physiol Meas 2021 Jun 11. Epub 2021 Jun 11.

Biomedical Engineering, TOBB Ekonomi ve Teknoloji Universitesi, Sogutozu Cad. No:43, Cankaya, 06560, TURKEY.

Objective: In this study, we conducted a comparative analysis of deep convolutional neural network (CNN) models in predicting Obstructive Sleep Apnea (OSA) using electrocardiograms. Unlike other studies in the literature, this study automatically extracts time-frequency features by using CNNs instead of manual feature extraction from ECG recordings.

Approach: The proposed model generates scalogram and spectrogram representations by transforming preprocessed 30-sec ECG segments from time domain to the frequency domain using Continuous Wavelet Transform (CWT) and Short Time Fourier transform (STFT), respectively. Read More

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Real-Time and Adaptive Reservoir Computing With Application to Profile Prediction in Fusion Plasma.

IEEE Trans Neural Netw Learn Syst 2021 Jun 11;PP. Epub 2021 Jun 11.

Nuclear fusion is a promising alternative to address the problem of sustainable energy production. The tokamak is an approach to fusion based on magnetic plasma confinement, constituting a complex physical system with many control challenges. We study the characteristics and optimization of reservoir computing (RC) for real-time and adaptive prediction of plasma profiles in the DIII-D tokamak. Read More

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Using deep learning to identify bladder cancers with FGFR-activating mutations from histology images.

Cancer Med 2021 Jun 10. Epub 2021 Jun 10.

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.

Background: In recent years, the fibroblast growth factor receptor (FGFR) pathway has been proven to be an important therapeutic target in bladder cancer. FGFR-targeted therapies are effective for patients with FGFR mutation, which can be discovered through genetic sequencing. However, genetic sequencing is not commonly performed at diagnosis, whereas a histologic assessment of the tumor is. Read More

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Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model.

Mach Learn Med Eng Cardiovasc Health Intravasc Imaging Comput Assist Stenting (2019) 2019 12;11794:167-174. Epub 2019 Oct 12.

Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard medical school, Boston, USA.

An abdominal aortic aneurysm (AAA) is a ballooning of the abdominal aorta, that if not treated tends to grow and rupture. Computed Tomography Angiography (CTA) is the main imaging modality for the management of AAAs, and segmenting them is essential for AAA rupture risk and disease progression assessment. Previous works have shown that Convolutional Neural Networks (CNNs) can accurately segment AAAs, but have the limitation of requiring large amounts of annotated data to train the networks. Read More

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October 2019

COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans.

Front Artif Intell 2021 25;4:598932. Epub 2021 May 25.

Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada.

The newly discovered Coronavirus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. The rapid outbreak of this disease has overwhelmed health care infrastructures and arises the need to allocate medical equipment and resources more efficiently. The early diagnosis of this disease will lead to the rapid separation of COVID-19 and non-COVID cases, which will be helpful for health care authorities to optimize resource allocation plans and early prevention of the disease. Read More

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Learned Gradient Compression for Distributed Deep Learning.

IEEE Trans Neural Netw Learn Syst 2021 Jun 10;PP. Epub 2021 Jun 10.

Training deep neural networks on large datasets containing high-dimensional data requires a large amount of computation. A solution to this problem is data-parallel distributed training, where a model is replicated into several computational nodes that have access to different chunks of the data. This approach, however, entails high communication rates and latency because of the computed gradients that need to be shared among nodes at every iteration. Read More

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GNINA 1.0: molecular docking with deep learning.

J Cheminform 2021 Jun 9;13(1):43. Epub 2021 Jun 9.

Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1. Read More

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DINs: Deep Interactive Networks for Neurofibroma Segmentation in Neurofibromatosis Type 1 on Whole-Body MRI.

IEEE J Biomed Health Inform 2021 Jun 9;PP. Epub 2021 Jun 9.

Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition syndrome that involves the central and peripheral nervous systems. Accurate detection and segmentation of neurofibromas are essential for assessing tumor burden and longitudinal tumor size changes. Automatic convolutional neural networks (CNNs) are sensitive and vulnerable as tumors' variable anatomical location and heterogeneous appearance on MRI. Read More

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Automatic Facial Recognition of Williams-Beuren Syndrome Based on Deep Convolutional Neural Networks.

Front Pediatr 2021 19;9:648255. Epub 2021 May 19.

Department of Pediatric Cardiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Cardiovascular Institute, Guangzhou, China.

Williams-Beuren syndrome (WBS) is a rare genetic syndrome with a characteristic "elfin" facial gestalt. The "elfin" facial characteristics include a broad forehead, periorbital puffiness, flat nasal bridge, short upturned nose, wide mouth, thick lips, and pointed chin. Recently, deep convolutional neural networks (CNNs) have been successfully applied to facial recognition for diagnosing genetic syndromes. Read More

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Classification of human body motions using an ultra-wideband pulse radar.

Technol Health Care 2021 May 24. Epub 2021 May 24.

Background: The motion or gestures of a person are primarily recognized by detecting a specific object and the change in its position from image information obtained via an image sensor. However, the use of such systems is limited due to privacy concerns.

Objective: To overcome these concerns, this study proposes a radar-based motion recognition method. Read More

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Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI with deep learning.

Pediatr Res 2021 Jun 5. Epub 2021 Jun 5.

Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hang Zhou, China.

Background: Differentiating acute bilirubin encephalopathy (ABE) from non-ABE in neonates with hyperbilirubinemia (HB) from routine magnetic resonance imaging (MRI) is extremely challenging since both conditions demonstrate similar T1 hyperintensities. To this end, we investigated whether the integration of multimodal MRI from routine clinical scans with deep-learning approaches could improve diagnostic performance.

Methods: A total of 75 neonates with ABE and 75 neonates with HB (non-ABE) were included in the study. Read More

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

Med Image Anal 2021 May 16;72:102098. Epub 2021 May 16.

Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address:

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-sharing convolutional neural networks (CNNs) to k-space data without taking into consideration the k-space data's spatial frequency properties, leading to ineffective learning of the image reconstruction models. Moreover, complementary information of spatially adjacent slices is often ignored in existing deep learning methods. Read More

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Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data.

Sci Rep 2021 Jun 4;11(1):11895. Epub 2021 Jun 4.

Centrum Wiskunde and Informatica, Amsterdam, The Netherlands.

Synchrotron X-ray tomography enables the examination of the internal structure of materials at submicron spatial resolution and subsecond temporal resolution. Unavoidable experimental constraints can impose dose and time limits on the measurements, introducing noise in the reconstructed images. Convolutional neural networks (CNNs) have emerged as a powerful tool to remove noise from reconstructed images. Read More

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Identifying the histologic subtypes of non-small cell lung cancer with computed tomography imaging: a comparative study of capsule net, convolutional neural network, and radiomics.

Quant Imaging Med Surg 2021 Jun;11(6):2756-2765

School of Biomedical Engineering, Capital Medical University, Beijing, China.

Background: Discriminating the subtypes of non-small cell lung cancer (NSCLC) based on computed tomography (CT) images is a challenging task for radiologists. Although several machine learning methods such as radiomics, and deep learning methods such as convolutional neural networks (CNNs) have been proposed to explore the problem, large sample sizes are required for effective training, and this may not be easily achieved in single-center datasets.

Methods: In this study, an automated subtype recognition model with capsule net (CapsNet) was developed for the subtype discrimination of NSCLC. Read More

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A pilot study: quantify lung volume and emphysema extent directly from 2-D scout images.

Med Phys 2021 Jun 2. Epub 2021 Jun 2.

Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA.

Purpose: The potential to compute volume metrics of emphysema from planar scout images was investigated in this study. The successful implementation of this concept will have a wide impact in different fields, and specifically, maximize the diagnostic potential of the planar medical images.

Methods: We investigate our premise using a well-characterized chronic obstructive pulmonary disease (COPD) cohort. Read More

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Cyber-Physical System for Environmental Monitoring Based on Deep Learning.

Sensors (Basel) 2021 May 24;21(11). Epub 2021 May 24.

Departamento de Zoología, Facultad de Biología, Universidad de Sevilla, Avenida de la Reina Mercedes, s/n, 41012 Sevilla, Spain.

Cyber-physical systems (CPS) constitute a promising paradigm that could fit various applications. Monitoring based on the Internet of Things (IoT) has become a research area with new challenges in which to extract valuable information. This paper proposes a deep learning classification sound system for execution over CPS. Read More

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Artificial Intelligence-Based Recognition of Different Types of Shoulder Implants in X-ray Scans Based on Dense Residual Ensemble-Network for Personalized Medicine.

J Pers Med 2021 May 27;11(6). Epub 2021 May 27.

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea.

Re-operations and revisions are often performed in patients who have undergone total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RTSA). This necessitates an accurate recognition of the implant model and manufacturer to set the correct apparatus and procedure according to the patient's anatomy as personalized medicine. Owing to unavailability and ambiguity in the medical data of a patient, expert surgeons identify the implants through a visual comparison of X-ray images. Read More

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An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network.

Sensors (Basel) 2021 May 27;21(11). Epub 2021 May 27.

School of Microelectronics, Shandong University, Jinan 250100, China.

Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they usually require more training samples and higher computational complexity than the classic methods. This work focuses on training a novel condensed 2-channel (2-ch) CNN with few training samples for efficient and accurate iris identification and verification. Read More

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High Accuracy WiFi-Based Human Activity Classification System with Time-Frequency Diagram CNN Method for Different Places.

Sensors (Basel) 2021 May 30;21(11). Epub 2021 May 30.

Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan.

Older people are very likely to fall, which is a significant threat to the health. However, falls are preventable and are not necessarily an inevitable part of aging. Many different fall detection systems have been developed to help people avoid falling. Read More

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Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks.

Materials (Basel) 2021 May 25;14(11). Epub 2021 May 25.

Faculty of Civil and Environmental Engineering and Architecture, Rzeszów University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland.

The aim of the following paper is to discuss a newly developed approach for the identification of vibration mode shapes of multilayer composite structures. To overcome the limitations of the approaches based on image analysis (two-dimensional structures, high spatial resolution of mode shapes description), convolutional neural networks (CNNs) are applied to create a three-dimensional mode shapes identification algorithm with a significantly reduced number of mode shape vector coordinates. The CNN-based procedure is accurate, effective, and robust to noisy input data. Read More

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Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach.

Diagnostics (Basel) 2021 May 18;11(5). Epub 2021 May 18.

Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India.

COVID-19 is a disease caused by the SARS-CoV-2 virus. The COVID-19 virus spreads when a person comes into contact with an affected individual. This is mainly through drops of saliva or nasal discharge. Read More

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Automatic vs. Human Recognition of Pain Intensity from Facial Expression on the X-ITE Pain Database.

Sensors (Basel) 2021 May 10;21(9). Epub 2021 May 10.

Department of Medical Psychology, Ulm University, 89081 Ulm, Germany.

Prior work on automated methods demonstrated that it is possible to recognize pain intensity from frontal faces in videos, while there is an assumption that humans are very adept at this task compared to machines. In this paper, we investigate whether such an assumption is correct by comparing the results achieved by two human observers with the results achieved by a Random Forest classifier (RFc) baseline model (called RFc-BL) and by three proposed automated models. The first proposed model is a Random Forest classifying descriptors of Action Unit (AU) time series; the second is a modified MobileNetV2 CNN classifying face images that combine three points in time; and the third is a custom deep network combining two CNN branches using the same input as for MobileNetV2 plus knowledge of the RFc. Read More

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Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning.

Diagnostics (Basel) 2021 May 17;11(5). Epub 2021 May 17.

Technology Innovation and Engineering Education (TIEE), College of Engineering, Qatar University, Doha 2713, Qatar.

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder-Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. Read More

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LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products.

Sensors (Basel) 2021 May 22;21(11). Epub 2021 May 22.

The Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.

The remarkable success of convolutional neural networks (CNNs) in computer vision tasks is shown in large-scale datasets and high-performance computing platforms. However, it is infeasible to deploy large CNNs on resource constrained platforms, such as embedded devices, on account of the huge overhead. To recognize the label numbers of industrial black material product and deploy deep CNNs in real-world applications, this research uses an efficient method to simultaneously (a) reduce the network model size and (b) lower the amount of calculation without compromising accuracy. Read More

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