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Adaptive Local Ternary Pattern on Parameter Optimized-Faster Region Convolutional Neural Network for Pulmonary Emphysema Diagnosis

Sumita Mondal, Anup K. Sadhu, Pranab Kumar Dutta
2021 IEEE Access  
The severity of the disease may extend to a stage where one can risk their life emphasizing the early detection of emphysema.  ...  The enhancement of pattern formation and deep classification is accomplished by the Improved Red Deer Algorithm (IRDA), which helps to tune the significant parameters that have a positive influence on  ...  [24] have presented a novel multi-scale residual network that included two channels of raw CT image.  ... 
doi:10.1109/access.2021.3105114 fatcat:4gptovtoqbakpo4jdojtgkesya

Artificial Intelligence in COPD: New Venues to Study a Complex Disease

Raúl San José Estépar
2020 Barcelona Respiratory Network  
Artificial intelligence (AI) has revolutionized how we can use clinical, imaging, and molecular data to understand and model complex systems.  ...  Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease that can benefit from novel approaches to understanding its evolution and divergent trajectories.  ...  EmphysEma subtyping Quantification of emphysema on CT is probably today the most employed and reliable image-based biomarker 16 .  ... 
doi:10.23866/brnrev:2019-0014 pmid:33521399 pmcid:PMC7842269 fatcat:f4vtut2ckvfxvnn7uvcamwhy5a

Front Matter: Volume 10575

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
using a Base 36 numbering system employing both numerals and letters.  ...  The papers reflect the work and thoughts of the authors and are published herein as submitted.  ...  a large scale evaluation 10575 08 Detecting mammographically occult cancer in women with dense breasts using Radon Cumulative Distribution Transform: a preliminary analysis 10575 09 Deriving stable multi-parametric  ... 
doi:10.1117/12.2315758 fatcat:kqpt2ugrxrgx7m5rhasawarque

Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear [chapter]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
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  ...  * M-109 SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks Md.  ... 
doi:10.1007/978-3-030-00928-1_1 fatcat:ypoj3zplm5awljf6u5c2spgiea

Knowledge-based Analysis for Mortality Prediction from CT Images [article]

Hengtao Guo, Uwe Kruger, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
2019 arXiv   pre-print
This paper introduces a knowledge-based analytical method using deep convolutional neural network (CNN) for all-cause mortality prediction.  ...  It constitutes a collaborative framework that utilizes both imaging features and anatomical information, instead of completely relying on automatic feature extraction.  ...  The authors would also like to thank NVIDIA Corporation for the donation of the Titan Xp GPU used for this research.  ... 
arXiv:1902.07687v1 fatcat:xakggfxhvrfvxn6cnrnueoqsdi

Progress in the imaging of COPD: quantitative and functional evaluation

Li Fan, Xiuxiu Zhou, Yi Xia, Yu Guan, Di Zhang, ZhaoBin Li, Shiyuan Liu
2019 Chinese Journal of Academic Radiology  
The main characteristic of COPD is the heterogeneity of disease, manifesting as emphysema, functional small airways disease (fSAD) and large airway diseases.  ...  Chronic obstructive pulmonary disease (COPD) is a common, preventable, and treatable disease, which has caused serious social and economic burden.  ...  It has been reported that hybrid airway segmentation using multi-scale tubular structure filters and texture analysis on 3D chest CT scans has been performed successfully in the Korean Obstructive Lung  ... 
doi:10.1007/s42058-019-00007-0 fatcat:mwl5sj7w3fb5rbngtda2q3otjy

2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24

2020 IEEE journal of biomedical and health informatics  
, J., and Rubin, D. 2570-2579 Jiang, D., see 2473-2480 Jiang, H., see 2798-2805 Jiang, H., Yang, M., Chen, X., Li, M., Li, Y., and Wang, J., miRTMC: A miRNA Target Prediction Method Based on Matrix  ...  ., and Inan, O.T., 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  ...  ., +, JBHI Jan. 2020 194-204 Pulmonary Textures Classification via a Multi-Scale Attention Network.  ... 
doi:10.1109/jbhi.2020.3048808 fatcat:iifrkwtzazdmboabdqii7x5ukm

Knowledge-based Analysis for Mortality Prediction from CT Images

Hengtao Guo, Uwe Kruger, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
2019 IEEE journal of biomedical and health informatics  
This paper introduces a knowledge-based analytical method using deep convolutional neural network (CNN) for all-cause mortality prediction.  ...  It constitutes a collaborative framework that utilizes both imaging features and anatomical information, instead of completely relying on automatic feature extraction.  ...  The authors would also like to thank NVIDIA Corporation for the donation of the Titan Xp GPU used for this research.  ... 
doi:10.1109/jbhi.2019.2946066 pmid:31603807 pmcid:PMC7007835 fatcat:dvsle422wnblhmblnblifqy3ne

Computer analysis of computed tomography scans of the lung: a survey

I. Sluimer, A. Schilham, M. Prokop, B. van Ginneken
2006 IEEE Transactions on Medical Imaging  
at detection, classification and quantification of chest abnormalities.  ...  This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed  ...  The challenges for future research on emphysema quantification lie in quantification of emphysema patterns and prognosis of the disease.  ... 
doi:10.1109/tmi.2005.862753 pmid:16608056 fatcat:q5ysngkgozbbrfwn7tbb2fvunu

Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture

Mohamed Chetoui, Moulay A. Akhloufi, Bardia Yousefi, El Mostafa Bouattane
2021 Big Data and Cognitive Computing  
We propose two classifications problems, (i) a binary classification to classify COVID-19 and normal cases and (ii) a multiclass classification for COVID-19, pneumonia and normal.  ...  The model is trained on a subset of the National Institute of Health (NIH) dataset using swish activation, thus improving the training accuracy to detect COVID-19 and other pneumonia.  ...  Data Availability Statement: The data used in this work come mainly for public datasets. Please see the section describing the datasets.  ... 
doi:10.3390/bdcc5040073 doaj:e444bfe41dd04ae1a8283436fed90aad fatcat:s27kctg4qjevlolywdhcevvrhi

Functional Imaging: CT and MRI

Edwin J.R. van Beek, Eric A. Hoffman
2008 Clinics in Chest Medicine  
Techniques using spirometric controlled MDCT allow for quantification of presence and distribution of parenchymal and airway pathology, Xenon gas can be employed to assess regional ventilation of the lungs  ...  Advances in magnetic resonance imaging (MRI) of the lung include gadolinium-enhanced perfusion imaging and hyperpolarized helium imaging, which can allow imaging of pulmonary ventilation and .measurement  ...  of parenchymal attenuation and texture and finally a regional quantification of ventilation and perfusion parameters.  ... 
doi:10.1016/j.ccm.2007.12.003 pmid:18267192 pmcid:PMC2435287 fatcat:hos7dakjqzgt5at7pzouszsnrm

Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays [article]

Yan Han, Chongyan Chen, Liyan Tang, Mingquan Lin, Ajay Jaiswal, Song Wang, Ahmed Tewfik, George Shih, Ying Ding, Yifan Peng
2021 arXiv   pre-print
After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions.  ...  We evaluate the ChexRadiNet framework using three public datasets: NIH ChestX-ray, CheXpert, and MIMIC-CXR.  ...  It also was supported by the National Library of Medicine under Award No. 4R00LM013001.  ... 
arXiv:2011.12506v3 fatcat:ap3gemz7e5dpzmhjtzmojovsem

3d virtual pathohistology of lung tissue from COVID-19 patients based on phase contrast x-ray tomography

Marina Eckermann, Jasper Frohn, Marius Reichardt, Markus Osterhoff, Michael Sprung, Fabian Westermeier, Alexandar Tzankov, Mark Kühnel, Danny Jonigk, Tim Salditt
2020 eLife  
We present a three-dimensional (3d) approach for virtual histology and histopathology based on multi-scale phase contrast x-ray tomography, and use this to investigate the parenchymal architecture of unstained  ...  and allowing for full quantification of tissue remodeling.  ...  Furthermore, there is a mild centrilobular emphysema (original magnification 163 100×). In PC-CT, a network of thin septa, thrombi and emphysema, as well as a large empty blood 164 vessel appears.  ... 
doi:10.7554/elife.60408 pmid:32815517 pmcid:PMC7473770 fatcat:qvqxydpyn5cm5ly3swoet4vewe

FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images

Sarah E. Gerard, Taylor J. Patton, Gary E. Christensen, John E. Bayouth, Joseph M. Reinhardt
2018 IEEE Transactions on Medical Imaging  
We propose a supervised discriminative learning framework for simultaneous feature extraction and classification.  ...  The majority of fissure detection methods use feature descriptors that are hand-crafted, low-level, and have local spatial extent.  ...  Gerard received support from a Presidential Fellowship through the University of Iowa Graduate College and from a NASA Iowa Space Grant Consortium Fellowship.  ... 
doi:10.1109/tmi.2018.2858202 pmid:30106711 pmcid:PMC6318012 fatcat:z35swk2tabhb5prteyoelk33ee

Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation [article]

Ali Hatamizadeh
2020 arXiv   pre-print
that unifies CNNs and active contour models with learnable parameters for fast and robust object delineation, (3) a novel approach for disentangling edge and texture processing in segmentation networks  ...  Recently, convolutional neural networks (CNNs) have gained traction in the design of automated segmentation pipelines.  ...  [136] use a 2D edge attention guidance network to learn the edge attention representation in the earlier stages of the encoding process and transfer them to multi-scale decoding layers where they are  ... 
arXiv:2006.12706v1 fatcat:6jchhrv6zrhlhbpcak6fcbh4a4
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