11,693 results match your criteria deep convolutional


Deep multimodal learning for lymph node metastasis prediction of primary thyroid cancer.

Phys Med Biol 2022 Jan 18. Epub 2022 Jan 18.

School of Computer Science and Engineering, Wuhan Institute of Technology, #206 Guanggu No.1 Road, Donghu Hightech Development Zone, Wuhan, 430205, CHINA.

Incidence of primary thyroid cancer rises steadily over the past decades because of overdiagnosis and overtreatment through the improvement in imaging techniques for screening, especially in ultrasound examination. Metastatic status of lymph nodes is important for staging the type of primary thyroid cancer. Deep learning algorithms based on ultrasound images were thus developed to assist radiologists on the diagnosis of lymph node metastasis. Read More

View Article and Full-Text PDF
January 2022

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.

PLoS Comput Biol 2022 Jan 18;18(1):e1009797. Epub 2022 Jan 18.

Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America.

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2. Read More

View Article and Full-Text PDF
January 2022

Motor Imagery Classification Using Inter-Task Transfer Learning via A Channel-Wise Variational Autoencoder-based Convolutional Neural Network.

IEEE Trans Neural Syst Rehabil Eng 2022 Jan 18;PP. Epub 2022 Jan 18.

Highly sophisticated control based on a brain-computer interface (BCI) requires decoding kinematic information from brain signals. The forearm is a region of the upper limb that is often used in everyday life, but intuitive movements within the same limb have rarely been investigated in previous BCI studies. In this study, we focused on various forearm movement decoding from electroencephalography (EEG) signals using a small number of samples. Read More

View Article and Full-Text PDF
January 2022

High Accuracy Real-Time Multi-Gas Identification by a Batch-Uniform Gas Sensor Array and Deep Learning Algorithm.

ACS Sens 2022 Jan 18. Epub 2022 Jan 18.

Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

Semiconductor metal oxide (SMO) gas sensors are attracting great attention as next-generation environmental monitoring sensors. However, there are limitations to the actual application of SMO gas sensors due to their low selectivity. Although the electronic nose (E-nose) systems based on a sensor array are regarded as a solution for the selectivity issue, poor accuracy caused by the nonuniformity of the fabricated gas sensors and difficulty of real-time gas detection have yet to be resolved. Read More

View Article and Full-Text PDF
January 2022

Improving Diabetes-Related Biomedical Literature Exploration in the Clinical Decision-making Process via Interactive Classification and Topic Discovery: Methodology Development Study.

J Med Internet Res 2022 Jan 18;24(1):e27434. Epub 2022 Jan 18.

Epiconcept Company, Paris, France.

Background: The amount of available textual health data such as scientific and biomedical literature is constantly growing and becoming more and more challenging for health professionals to properly summarize those data and practice evidence-based clinical decision making. Moreover, the exploration of unstructured health text data is challenging for professionals without computer science knowledge due to limited time, resources, and skills. Current tools to explore text data lack ease of use, require high computational efforts, and incorporate domain knowledge and focus on topics of interest with difficulty. Read More

View Article and Full-Text PDF
January 2022

Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk.

J Med Internet Res 2022 Jan 18;24(1):e28749. Epub 2022 Jan 18.

Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.

Background: Crowdsourcing services, such as Amazon Mechanical Turk (AMT), allow researchers to use the collective intelligence of a wide range of web users for labor-intensive tasks. As the manual verification of the quality of the collected results is difficult because of the large volume of data and the quick turnaround time of the process, many questions remain to be explored regarding the reliability of these resources for developing digital public health systems.

Objective: This study aims to explore and evaluate the application of crowdsourcing, generally, and AMT, specifically, for developing digital public health surveillance systems. Read More

View Article and Full-Text PDF
January 2022

Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning.

Yonsei Med J 2022 Jan;63(Suppl):S63-S73

Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.

Purpose: In this paper, we propose deep-learning methodology with which to enhance the mass differentiation performance of convolutional neural network (CNN)-based architecture.

Materials And Methods: We differentiated breast mass lesions from gray-scale X-ray mammography images based on regions of interest (ROIs). Our dataset comprised breast mammogram images for 150 cases of malignant masses from which we extracted the mass ROI, and we composed a CNN-based deep learning model trained on this dataset to identify ROI mass lesions. Read More

View Article and Full-Text PDF
January 2022

Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network.

Int J Legal Med 2022 Jan 18. Epub 2022 Jan 18.

Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, 1347 GuangFu West Road, Shanghai, 200063, People's Republic of China.

In the forensic estimation of bone age, the pelvis is important for identifying the bone age of teenagers. However, studies on this topic remain insufficient as a result of lower accuracy due to the overlapping of pelvic organs in X-ray images. Segmentation networks have been used to automate the location of key pelvic areas and minimize restrictions like doubling images of pelvic organs to increase the accuracy of estimation. Read More

View Article and Full-Text PDF
January 2022

Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification.

Appl Intell (Dordr) 2022 Jan 13:1-15. Epub 2022 Jan 13.

Hunan University and State University of New York, Albany, NY USA.

Deep convolutional networks have been widely used for various medical image processing tasks. However, the performance of existing learning-based networks is still limited due to the lack of large training datasets. When a general deep model is directly deployed to a new dataset with heterogeneous features, the effect of domain shifts is usually ignored, and performance degradation problems occur. Read More

View Article and Full-Text PDF
January 2022

Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach.

Sci Rep 2022 Jan 17;12(1):786. Epub 2022 Jan 17.

Department of Radiation Medicine and Applied Sciences, University of California San Diego, 3960 Health Sciences Dr, Mail Code 0865, La Jolla, CA, USA.

Stereotactic radiosurgery planning for cerebral arteriovenous malformations (AVM) is complicated by the variability in appearance of an AVM nidus across different imaging modalities. We developed a deep learning approach to automatically segment cerebrovascular-anatomical maps from multiple high-resolution magnetic resonance imaging/angiography (MRI/MRA) sequences in AVM patients, with the goal of facilitating target delineation. Twenty-three AVM patients who were evaluated for radiosurgery and underwent multi-parametric MRI/MRA were included. Read More

View Article and Full-Text PDF
January 2022

Diagnosis of schizophrenia with functional connectome data: a graph-based convolutional neural network approach.

BMC Neurosci 2022 Jan 17;23(1). Epub 2022 Jan 17.

Department of Psychiatry, Jeonbuk National University, Medical School, Geonjiro 20, Jeonju, Korea.

Previous deep learning methods have not captured graph or network representations of brain structural or functional connectome data. To address this, we developed the BrainNet-Global Covariance Pooling-Attention Convolutional Neural Network (BrainNet-GA CNN) by incorporating BrainNetCNN and global covariance pooling into the self-attention mechanism. Resting-state functional magnetic resonance imaging data were obtained from 171 patients with schizophrenia spectrum disorders (SSDs) and 161 healthy controls (HCs). Read More

View Article and Full-Text PDF
January 2022

A deep convolutional neural network for estimating hemodynamic response function with reduction of motion artifacts in fNIRS.

J Neural Eng 2022 Jan 17. Epub 2022 Jan 17.

Research Center for Bioconvergence Analysis, Korea Basic Science Institute, 162 Yeongudanji-ro, Cheongwon-gu, Ochang-eup, Cheongju, 28119, Korea (the Republic of).

Objective: Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique for monitoring hemoglobin concentration changes in a non-invasive manner. However, subject movements are often significant sources of artifacts. While several methods have been developed for suppressing this confounding noise, the conventional techniques have limitations on optimal selections of model parameters across participants or brain regions. Read More

View Article and Full-Text PDF
January 2022

Deep-learning online EEG decoding brain-computer interface using error-related potentials recorded with a consumer-grade headset.

Biomed Phys Eng Express 2022 Jan 17. Epub 2022 Jan 17.

Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, GERMANY.

Brain-computer interfaces (BCIs) allow subjects with sensorimotor disability to interact with the environment. Non-invasive BCIs relying on EEG signals such as event-related potentials (ERPs) have been established as a reliable compromise between spatio-temporal resolution and patient impact, but limitations due to portability and versatility preclude their broad application. Here we describe a deep-learning augmented error-related potential (ErrP) discriminating BCI using a consumer-grade portable headset EEG, the Emotiv EPOC+. Read More

View Article and Full-Text PDF
January 2022

Development of an open-source measurement system to assess the areal bone mineral density of the proximal femur from clinical CT images.

Arch Osteoporos 2022 Jan 17;17(1):17. Epub 2022 Jan 17.

Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan.

Commercial software is generally needed to measure the areal bone mineral density (aBMD) of the proximal femur from clinical computed tomography (CT) images. This study developed and verified an open-source reproducible system to quantify CT-aBMD to screen osteoporosis using clinical CT images.

Purpose: For existing CT images acquired for various reasons other than osteoporosis, it might be beneficial to estimate areal BMD as assessed by dual-energy X-ray absorptiometry (DXA-based BMD) to ascertain the bone status based on DXA. Read More

View Article and Full-Text PDF
January 2022

GPS-Uber: a hybrid-learning framework for prediction of general and E3-specific lysine ubiquitination sites.

Brief Bioinform 2022 Jan 17. Epub 2022 Jan 17.

Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.

As an important post-translational modification, lysine ubiquitination participates in numerous biological processes and is involved in human diseases, whereas the site specificity of ubiquitination is mainly decided by ubiquitin-protein ligases (E3s). Although numerous ubiquitination predictors have been developed, computational prediction of E3-specific ubiquitination sites is still a great challenge. Here, we carefully reviewed the existing tools for the prediction of general ubiquitination sites. Read More

View Article and Full-Text PDF
January 2022

CX-ToM: Counterfactual explanations with theory-of-mind for enhancing human trust in image recognition models.

iScience 2022 Jan 11;25(1):103581. Epub 2021 Dec 11.

Beijing Institute for General AI (BIGAI), Tsinghua University, Peking University, Beijing 100871, China.

We propose , short for counterfactual explanations with theory-of-mind, a new explainable AI (XAI) framework for explaining decisions made by a deep convolutional neural network (CNN). In contrast to the current methods in XAI that generate explanations as a single shot response, we pose explanation as an iterative communication process, i.e. Read More

View Article and Full-Text PDF
January 2022

A pixel-wise annotated dataset of small overlooked indoor objects for semantic segmentation applications.

Data Brief 2022 Feb 5;40:107791. Epub 2022 Jan 5.

School of Engineering, University of Kent, Canterbury, UK.

The purpose of the dataset is to provide annotated images for pixel classification tasks with application to powered wheelchair users. As some of the widely available datasets contain only general objects, we introduced this dataset to cover the missing pieces, which can be considered as application-specific objects. However, these objects of interest are not only important for powered wheelchair users but also for indoor navigation and environmental understanding in general. Read More

View Article and Full-Text PDF
February 2022

CheXImageNet: a novel architecture for accurate classification of Covid-19 with chest x-ray digital images using deep convolutional neural networks.

Health Technol (Berl) 2022 Jan 11:1-12. Epub 2022 Jan 11.

Department of Computer Science & IT, University of Jammu, 180006 Jammu & Kashmir, India.

Many countries around the world have been influenced by Covid-19 which is a serious virus as it gets transmitted by human communication. Although, its syndrome is quite similar to the ordinary flu. The critical step involved in Covid-19 is the initial screening or testing of the infected patients. Read More

View Article and Full-Text PDF
January 2022

Research on Chest Disease Recognition Based on Deep Hierarchical Learning Algorithm.

J Healthc Eng 2022 7;2022:6996444. Epub 2022 Jan 7.

Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China.

Chest X-ray has become one of the most common ways in diagnostic radiology exams, and this technology assists expert radiologists with finding the patients at potential risk of cardiopathy and lung diseases. However, it is still a challenge for expert radiologists to assess thousands of cases in a short period so that deep learning methods are introduced to tackle this problem. Since the diseases have correlations with each other and have hierarchical features, the traditional classification scheme could not achieve a good performance. Read More

View Article and Full-Text PDF
January 2022

Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile.

J Healthc Eng 2022 7;2022:4715998. Epub 2022 Jan 7.

Department of Radiology, Faculty of Medicine, King Abdulaziz University Hospital, Jeddah, 21589, Saudi Arabia.

The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in various cell types or tissues. The advent of DNA microarray technology has enabled simultaneous intensive care of hundreds of gene expressions on a single chip, advancing cancer categorization. Read More

View Article and Full-Text PDF
January 2022

Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning.

Comput Struct Biotechnol J 2022 23;20:333-342. Epub 2021 Dec 23.

Department of VIP Medical Services, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.

HER2-positive breast cancer is a highly heterogeneous tumor, and about 30% of patients still suffer from recurrence and metastasis after trastuzumab targeted therapy. Predicting individual prognosis is of great significance for the further development of precise therapy. With the continuous development of computer technology, more and more attention has been paid to computer-aided diagnosis and prognosis prediction based on Hematoxylin and Eosin (H&E) pathological images, which are available for all breast cancer patients undergone surgical treatment. Read More

View Article and Full-Text PDF
December 2021

Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images.

Cognit Comput 2022 Jan 11:1-21. Epub 2022 Jan 11.

Department of Electrical Engineering, Qatar University, 2713 Doha, Qatar.

Novel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). Read More

View Article and Full-Text PDF
January 2022

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization.

Authors:
Juan Sun

Comput Intell Neurosci 2022 6;2022:9051058. Epub 2022 Jan 6.

Department of Music, Handan University, Handan City, Hebei Province 056005, China.

In this paper, we use a variational fuzzy neural network algorithm to conduct an in-depth analysis and research on the optimization of music intelligent marketing strategy. The music recommendation system proposed in this paper includes a user modelling module, audio feature extraction module, and recommendation algorithm module. The basic idea of the recommendation algorithm is as follows: firstly, the historical behavioural information of music users is collected, and the user preference model is constructed by using the method of matrix decomposition of the hidden semantic model; then, the audio resources in the system are preprocessed and the spectrum map that can represent the music features is extracted; the similarity between the user's preferred features and the music potential features are calculated to generate recommendations for the target user. Read More

View Article and Full-Text PDF
January 2022

Detection of Aerobics Action Based on Convolutional Neural Network.

Authors:
Siyu Zhang

Comput Intell Neurosci 2022 5;2022:1857406. Epub 2022 Jan 5.

Sangmyung University Seoul, 20 Hongjimun 2-gil, Jongno-gu, Seoul 03016, Republic of Korea.

To further improve the accuracy of aerobics action detection, a method of aerobics action detection based on improving multiscale characteristics is proposed. In this method, based on faster R-CNN and aiming at the problems existing in faster R-CNN, the feature pyramid network (FPN) is used to extract aerobics action image features. So, the low-level semantic information in the images can be extracted, and it can be converted into high-resolution deep-level semantic information. Read More

View Article and Full-Text PDF
January 2022

Survey on Diagnosing CORONA VIRUS from Radiography Chest X-ray Images Using Convolutional Neural Networks.

Wirel Pers Commun 2022 Jan 8:1-10. Epub 2022 Jan 8.

Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka India.

Corona Virus continues to harms its effects on the people lives across the globe. The screening of infected persons has to be identified is a vital step because it is a fast and low-cost way. Certain above mentioned things can be recognized by chest X-ray images that plays a significant role and also used for examining in detection of CORONA VIRUS(COVID-19). Read More

View Article and Full-Text PDF
January 2022

DeepCov19Net: Automated COVID-19 Disease Detection with a Robust and Effective Technique Deep Learning Approach.

New Gener Comput 2022 Jan 12:1-23. Epub 2022 Jan 12.

Electrical-Electronics Engineering Department, Technology Faculty, Firat University, Elazig, Turkey.

The new type of coronavirus disease, which has spread from Wuhan, China since the beginning of 2020 called COVID-19, has caused many deaths and cases in most countries and has reached a global pandemic scale. In addition to test kits, imaging techniques with X-rays used in lung patients have been frequently used in the detection of COVID-19 cases. In the proposed method, a novel approach based on a deep learning model named DeepCovNet was utilized to classify chest X-ray images containing COVID-19, normal (healthy), and pneumonia classes. Read More

View Article and Full-Text PDF
January 2022

Segmentation and classification on chest radiography: a systematic survey.

Vis Comput 2022 Jan 8:1-39. Epub 2022 Jan 8.

Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh 177005 India.

Chest radiography (X-ray) is the most common diagnostic method for pulmonary disorders. A trained radiologist is required for interpreting the radiographs. But sometimes, even experienced radiologists can misinterpret the findings. Read More

View Article and Full-Text PDF
January 2022

A pilot study of a deep learning approach to detect marginal bone loss around implants.

BMC Oral Health 2022 Jan 16;22(1):11. Epub 2022 Jan 16.

Department of Prosthodontics, Peking University School and Hospital of Stomatology and National Engineering Laboratory for Digital and Material Technology of Stomatology and Research Center of Engineering and Technology for Digital Dentistry of Ministry of Health and Beijing Key Laboratory of Digital Stomatology and National Clinical Research Center for Oral Diseases, 22 ZhongguancunNandajie, Haidian District, Beijing, 100081, China.

Background: Recently, there has been considerable innovation in artificial intelligence (AI) for healthcare. Convolutional neural networks (CNNs) show excellent object detection and classification performance. This study assessed the accuracy of an artificial intelligence (AI) application for the detection of marginal bone loss on periapical radiographs. Read More

View Article and Full-Text PDF
January 2022

Saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using ultrasound image.

Comput Methods Programs Biomed 2021 Dec 31;215:106612. Epub 2021 Dec 31.

School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.

Deep learning methods, especially convolutional neural networks, have advanced the breast lesion classification task using breast ultrasound (BUS) images. However, constructing a highly-accurate classification model still remains challenging due to complex pattern, relatively-low contrast and fuzzy boundary existing between lesion regions (i.e. Read More

View Article and Full-Text PDF
December 2021

Accurate real-time monitoring of fine dust using a densely connected convolutional networks with measured plasma emissions.

Chemosphere 2022 Jan 13:133604. Epub 2022 Jan 13.

Department of Aerospace Engineering, Seoul National University 1 Gwanakro, Gwanakgu, Seoul, 151-742, South Korea. Electronic address:

Accurate identification and monitoring of fine dust are emerging as a primary global issue for addressing the harmful effects of fine dust on public health. Identifying the source of fine dust is indispensable for ensuring the human lifespan as well as preventing environmental disasters. Here a simple yet effective spark-induced plasma spectroscopy (SIPS) unit combined with deep learning for real-time classification is verified as a fast and precise PM (particulate matter) source identification technique. Read More

View Article and Full-Text PDF
January 2022