Cargando…

Attention induction for a CT volume classification of COVID-19

PURPOSE: This study proposes a method to draw attention toward the specific radiological findings of coronavirus disease 2019 (COVID-19) in CT images, such as bilaterality of ground glass opacity (GGO) and/or consolidation, in order to improve the classification accuracy of input CT images. METHODS:...

Descripción completa

Detalles Bibliográficos
Autores principales:	Takateyama, Yusuke, Haruishi, Takahito, Hashimoto, Masahiro, Otake, Yoshito, Akashi, Toshiaki, Shimizu, Akinobu
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer International Publishing 2022
Materias:	Original Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9574825/ https://www.ncbi.nlm.nih.gov/pubmed/36251150 http://dx.doi.org/10.1007/s11548-022-02769-y

Ejemplares similares

Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning
por: Kato, Sota, et al.
Publicado: (2022)

Prognostic value of metabolic tumor volume of pretreatment (18)F-FAMT PET/CT in non-small cell lung Cancer
por: Kumasaka, Soma, et al.
Publicado: (2018)

Radiation Exposure from CT Examinations in Japan
por: Tsushima, Yoshito, et al.
Publicado: (2010)

Iodine concentration calculated by dual-energy computed tomography (DECT) as a functional parameter to evaluate thyroid metabolism in patients with hyperthyroidism
por: Binh, Duong Duc, et al.
Publicado: (2017)

Optimization of pneumonia CT classification model using RepVGG and spatial attention features
por: Zhang, Qinyi, et al.
Publicado: (2023)

Effect of a Novel, Energy-Dense, Low-Volume Nutritional Food in the Treatment of Superior Mesenteric Artery Syndrome
por: Akashi, Tetsuro, et al.
Publicado: (2021)

Contour-enhanced attention CNN for CT-based COVID-19 segmentation
por: Karthik, R., et al.
Publicado: (2022)

CT screening for COVID-19 in asymptomatic patients before hospital admission
por: Uchida, Sho, et al.
Publicado: (2021)

Genomic Characterization and Phylogenetic Classification of Bovine Coronaviruses Through Whole Genome Sequence Analysis
por: Suzuki, Tohru, et al.
Publicado: (2020)

Densely connected attention network for diagnosing COVID-19 based on chest CT
por: Fu, Yu, et al.
Publicado: (2021)

MA-Net:Mutex attention network for COVID-19 diagnosis on CT images
por: Zheng, BingBing, et al.
Publicado: (2022)

FAM: focal attention module for lesion segmentation of COVID-19 CT images
por: Wu, Xiaoxin, et al.
Publicado: (2022)

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification
por: Yang, Dongrong, et al.
Publicado: (2022)

Automatic Semicircular Canal Segmentation of CT Volumes Using Improved 3D U-Net with Attention Mechanism
por: Wu, Hongcheng, et al.
Publicado: (2021)

Prediction of Large Joint Destruction in Patients With Rheumatoid Arthritis Using (18)F-FDG PET/CT and Disease Activity Score
por: Suto, Takahito, et al.
Publicado: (2016)

The effect of abolishing instructions to fast prior to contrast-enhanced CT on the incidence of acute adverse reactions
por: Tsushima, Yoshito, et al.
Publicado: (2020)

Multiclass Convolution Neural Network for Classification of COVID-19 CT Images
por: Woan Ching, Serena Low, et al.
Publicado: (2022)

COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold
por: Rao, Yunbo, et al.
Publicado: (2023)

SCTV-UNet: a COVID-19 CT segmentation network based on attention mechanism
por: Liu, Xiangbin, et al.
Publicado: (2023)

Attention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography images
por: Özdemir, Özgür, et al.
Publicado: (2022)

E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network
por: Ahila, T., et al.
Publicado: (2022)

Diagnostic Utility of the PD-L1 Immunostaining in Biopsy Specimens of Patients with Biliary Tract Neoplasms
por: Matsumoto, Kazuyuki, et al.
Publicado: (2022)

Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning
por: Li, Xiaoshuo, et al.
Publicado: (2021)

Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning
por: Qiblawey, Yazan, et al.
Publicado: (2021)

Improving COVID-19 CT classification of CNNs by learning parameter-efficient representation
por: Xu, Yujia, et al.
Publicado: (2023)

Segmentation of lung lobes and lesions in chest CT for the classification of COVID-19 severity
por: Khomduean, Prachaya, et al.
Publicado: (2023)

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans
por: Wen, Cuihong, et al.
Publicado: (2023)

Attention-based VGG-16 model for COVID-19 chest X-ray image classification
por: Sitaula, Chiranjibi, et al.
Publicado: (2020)

Channel-spatial attention network for fewshot classification
por: Zhang, Yan, et al.
Publicado: (2019)

Optimizing audiovisual itch induction: the role of attention and expectancy
por: van Laarhoven, A.I.M., et al.
Publicado: (2019)

Inductive Document Network Embedding with Topic-Word Attention
por: Brochier, Robin, et al.
Publicado: (2020)

Development and validation of an open-source tool for opportunistic screening of osteoporosis from hip CT images: a multicentre study of 978 hips
por: Uemura, Keisuke, et al.
Publicado: (2023)

Severe COVID-19 Pneumonia: Assessing Inflammation Burden with Volume-rendered Chest CT
por: Tang, Lei, et al.
Publicado: (2020)

Data on correlation between CT-derived and MRI-derived myocardial extracellular volume
por: Kurita, Yoshie, et al.
Publicado: (2016)

Deep 3D attention CLSTM U-Net based automated liver segmentation and volumetry for the liver transplantation in abdominal CT volumes
por: Jeong, Jin Gyo, et al.
Publicado: (2022)

Understanding of COVID-19 Pathology: Much More Attention to Plasma Proteins
por: Nishibori, Masahiro, et al.
Publicado: (2021)

Rapid identification of COVID-19 severity in CT scans through classification of deep features
por: Yu, Zekuan, et al.
Publicado: (2020)

CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19)
por: Li, Kunwei, et al.
Publicado: (2020)

An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics
por: Zaffino, Paolo, et al.
Publicado: (2021)

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_paa9m09u14d8t8l0v3u2blpd4h