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A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
Preserved Fulltext
for Histopathology 351 Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-Phase CT Images 352 Domain and Geometry Agnostic CNNs for Left Atrium Segmentation ...
Deep Neural Network and Statistical Shape Model for Pancreas Segmentation 327 One-pass Multi-task Convolutional Neural Networks for Efficient Brain Tumor Segmentation 329 MRI Measurement of Placental ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
Front Matter: Volume 10575
2018
Medical Imaging 2018: Computer-Aided Diagnosis
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The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee. ...
Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library. ...
35
Localization of lung fields in HRCT images using a deep convolution neural network
[10575-112]
10575 36
Deep neural network convolution (NNC) for three-class classification of diffuse lung
disease ...
doi:10.1117/12.2315758
fatcat:kqpt2ugrxrgx7m5rhasawarque
Artificial Intelligence in COPD: New Venues to Study a Complex Disease
2020
Barcelona Respiratory Network
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This review provides an introduction to AI and deep learning and presents some recent successes in applying AI in COPD. ...
Finally, we will discuss some of the opportunities, challenges, and limitations for AI applications in the context of COPD. ...
Convolutional neural networks have enabled image interpretation at different scales. ...
doi:10.23866/brnrev:2019-0014
pmid:33521399
pmcid:PMC7842269
fatcat:f4vtut2ckvfxvnn7uvcamwhy5a
CUSTOMIZED CONVOLUTION NEURAL NETWORK FOR MULTI-CLASS LUNG ABNORMALITY CLASSIFICATION FROM CT IMAGES
2021
Information Technology in Industry
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In this paper, we propose customized CNN based multi-class lung abnormality classifier from CT images. ...
Recently, medical image analysis utilizes Convolution Neural Network(CNN) to improve the outcome of clinical diagnosis. ...
Figure 2 . 2 Architecture Of The Proposed Multi-Class Lung Abnormality Classification System
Figure 3 . 3 Accuracy and Loss of the Proposed Multiclass Deep Neural Network based on custom CNN. ...
doi:10.17762/itii.v9i1.93
fatcat:jneie2xzprfcdomugmleaxnyou
Front Matter: Volume 11317
2020
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
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The diverse sessions included Keynote and Invited Talk, Bone and Skeletal Imaging, Segmentation, Registration and Decision-making, Cardiac Imaging and Nanoparticle Imaging, Deep Convolutional Neural Networks ...
in Molecular, Structural, and Functional Imaging, Innovations in Image Processing, Neurological Imaging, Novel Imaging Techniques and Applications, Ocular and Optical Imaging, Vascular and Pulmonary Imaging ...
CT using a convolutional neural network
0T Supervised machine learning for region assignment of zebrafish brain nuclei based on computational assessment of cell neighborhoods 11317 0U Deep learning ...
doi:10.1117/12.2570187
fatcat:5hec55iwfneuhbhtjbqjcf2jqu
Adaptive Local Ternary Pattern on Parameter Optimized-Faster Region Convolutional Neural Network for Pulmonary Emphysema Diagnosis
2021
IEEE Access
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INDEX TERMS Adaptive local ternary pattern, fuzzy C means clustering, modified deep learning, parameter optimized-faster region convolutional neural network, pulmonary emphysema. ...
The progressive destruction of emphysema can be assessed by Computed Tomography (CT) scans and pulmonary function tests. ...
Sadhu for helping with the imaging samples. The authors gratefully acknowledge the facilities provided by IIT Kharagpur to carry out this research. ...
doi:10.1109/access.2021.3105114
fatcat:4gptovtoqbakpo4jdojtgkesya
An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification
2021
Turkish Journal of Electrical Engineering and Computer Sciences
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This paper introduces an 5 end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. ...
A dataset consisting of 21328 image patches of 128 CT scans 9 with five classes is taken to train and assess the proposed model. ...
Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks. arXiv preprint arXiv:200908864. 2020. [35] Martinez JB, Gill G. ...
doi:10.3906/elk-2106-31
fatcat:wmddosausjbrjdnrlilwayqgey
Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear
[chapter]
2018
Lecture Notes in Computer Science
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Recurrent Neural Networks for Classification of Focal Liver Lesions in
Multi-Phase CT Images
Liang Dong; Lanfen Lin*; Hongjie Hu; Qiaowei Zhang; Qingqing Chen; Yutaro Iwamoto; Xian-Hua Han;
Yen-Wei ...
Decoding from Functional MRI using Long Short-term Memory Recurrent Neural Networks
Hongming Li*; Yong Fan
T-96
Identification of Multi-scale Hierarchical Brain Functional Networks using Deep Matrix ...
T-129 Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training Wen ...
doi:10.1007/978-3-030-00928-1_1
fatcat:ypoj3zplm5awljf6u5c2spgiea
Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19
2020
Machine Vision and Applications
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This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. ...
Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. ...
DFCNet based on deep fully convolutional neural network was proposed by Masood et al. [142] , targeting pulmonary cancer detection and stage classification in CT images. ...
doi:10.1007/s00138-020-01101-5
pmid:32834523
pmcid:PMC7386599
fatcat:tkkylrptc5hkpoj52hjs3kuttu
Front Matter: Volume 9785
2016
Medical Imaging 2016: Computer-Aided Diagnosis
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The CID Number appears on each page of the manuscript. ...
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. ...
[9785-103]
POSTERS: LUNG AND CHEST
9785 2W
A novel approach for tuberculosis screening based on deep convolutional neural networks
[9785-104]
9785 2X
Ensemble lymph node detection from CT volumes ...
doi:10.1117/12.2240961
dblp:conf/micad/X16
fatcat:b5addnksdrgp3ixwvbjt53xeqe
Segmentation label propagation using deep convolutional neural networks and dense conditional random field
2016
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
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Availability and accessibility of large-scale annotated medical image datasets play an essential role in robust supervised learning of medical image analysis. ...
The proposed constrained segmentation propagation algorithm combines the cues from the initial annotations, deep convolutional neural networks and a dense fully-connected Conditional Random Field (CRF) ...
In this paper, we propose a segmentation propagation algorithm that combines the cues from the initial or partial manual annotations, deep convolutional neural networks (CNN) based single pixel classification ...
doi:10.1109/isbi.2016.7493497
dblp:conf/isbi/GaoXLWNSM16
fatcat:cawik5nmsjgpzdv2edqw6wfpbm
Survey of the Detection and Classification of Pulmonary Lesions via CT and X-Ray
[article]
2020
arXiv
pre-print
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This article reviews pulmonary CT and X-ray image detection and classification in the last decade. ...
It also provides an overview of the detection of lung nodules, pneumonia, and other common lung lesions based on the imaging characteristics of various lesions. ...
As such, this review focuses on methods based on CT and X-ray images for the detection and classification of pulmonary lesions and summarizes the pulmonary CT and X-ray image datasets obtained in the last ...
arXiv:2012.15442v1
fatcat:m6hwcww62res3pjzytmvglp7j4
2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24
2020
IEEE journal of biomedical and health informatics
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., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre, C., JBHI Jan ...
JBHI Feb. 2020 614-625 Hoog Antink, C., Mai, Y., Aalto, R., Bruser, C., Leonhardt, S., Oksala, N., and Vehkaoja, A., Ballistocardiography Can Estimate Beat-to-Beat Heart Rate Accurately at Night in ...
., +, JBHI May 2020 1447-1455 Pulmonary Textures Classification via a Multi-Scale Attention Network. Blood Cell Classification Based on Hyperspectral Imaging With Modulated Gabor and CNN. ...
doi:10.1109/jbhi.2020.3048808
fatcat:iifrkwtzazdmboabdqii7x5ukm
Segmentation of the Airway Tree from Chest CT using Tiny Atrous Convolutional Network
2021
IEEE Access
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INDEX TERMS Airway classification, convolutional neural network, deep learning, semantic segmentation, CT, medical image. ...
In order to take into account the multi-scale changes of the airway and achieve accurate airway segmentation, we design an end-to-end Tiny Atrous Convolutional Network (TACNet) based on 3D convolution ...
of convolutional neural network (CNN) in image segmentation appeared in large quantities. ...
doi:10.1109/access.2021.3059680
fatcat:g55kciuonjfu3is75zyv4546oq
Deep learning in interstitial lung disease—how long until daily practice
2020
European Radiology
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Developing a convolutional neuronal network (CNN) that could be deployed on any computer station and be accessible to non-academic centers is the next frontier that needs to be crossed. ...
lung disease. • Developing an accessible algorithm that could be deployed on any computer station and be used in non-academic centers is the next frontier in the early diagnosis of idiopathic pulmonary ...
et al used a deep neural network on graphics processing unit to recognize traffic signs images. ...
doi:10.1007/s00330-020-06986-4
pmid:32537728
fatcat:vcsl7jxehnfyfcyhh4esnu7774
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