20.7.2.1 Pneumonia, Lung Analysis, Flu, COVID

Chapter Contents (Back)
Pneumonia. Lungs. Medical, Applications.

Pattichis, M.S., Cacoullos, T., Soliz, P.[Peter],
New models for region of interest reader classification analysis in chest radiographs,
PR(42), No. 6, June 2009, pp. 1058-1066.
Elsevier DOI 0902
Region of interest classification; Chest radiographs; ROC analysis; Binary classification; Pneumoconiosis; Modeling biomedical systems; Logic, set theory, and algebra; Mathematical procedures and computer techniques BibRef

Zhao, W.[Wei], Xu, R.[Rui], Hirano, Y.S.[Yasu-Shi], Tachibana, R.[Rie], Kido, S.[Shoji], Suganuma, N.[Narufumi],
Classification of Pneumoconiosis on HRCT Images for Computer-Aided Diagnosis,
IEICE(E96-D), No. 4, April 2013, pp. 836-844.
WWW Link. 1304
BibRef

Ouyang, X., Huo, J., Xia, L., Shan, F., Liu, J., Mo, Z., Yan, F., Ding, Z., Yang, Q., Song, B., Shi, F., Yuan, H., Wei, Y., Cao, X., Gao, Y., Wu, D., Wang, Q., Shen, D.,
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia,
MedImg(39), No. 8, August 2020, pp. 2595-2605.
IEEE DOI 2008
Lung, Computed tomography, Diseases, Hospitals, Radiology, Image segmentation, COVID-19, COVID-19 Diagnosis, Online Attention, Dual Sampling Strategy BibRef

Kang, H., Xia, L., Yan, F., Wan, Z., Shi, F., Yuan, H., Jiang, H., Wu, D., Sui, H., Zhang, C., Shen, D.,
Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning,
MedImg(39), No. 8, August 2020, pp. 2606-2614.
IEEE DOI 2008
Lung, Computed tomography, Feature extraction, Hospitals, Testing, COVID-19, COVID-19, Pneumonia, Chest computed tomography (CT), Multi-view representation learning BibRef

Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Zheng, C.,
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT,
MedImg(39), No. 8, August 2020, pp. 2615-2625.
IEEE DOI 2008
Computed tomography, Lung, Lesions, Machine learning, Training, Diseases, COVID-19, COVID-19, CT, DeCoVNet BibRef

Fan, D., Zhou, T., Ji, G., Zhou, Y., Chen, G., Fu, H., Shen, J., Shao, L.,
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images,
MedImg(39), No. 8, August 2020, pp. 2626-2637.
IEEE DOI 2008
Computed tomography, Image segmentation, Lung, Training, Data models, Diseases, X-rays, COVID-19, COVID-19, CT image, infection segmentation, semi-supervised learning BibRef

Zhou, L., Li, Z., Zhou, J., Li, H., Chen, Y., Huang, Y., Xie, D., Zhao, L., Fan, M., Hashmi, S., Abdelkareem, F., Eiada, R., Xiao, X., Li, L., Qiu, Z., Gao, X.,
A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis,
MedImg(39), No. 8, August 2020, pp. 2638-2652.
IEEE DOI 2008
Computed tomography, Solid modeling, Lung, Image segmentation, COVID-19, COVID-19, computerized tomography BibRef

Wang, G., Liu, X., Li, C., Xu, Z., Ruan, J., Zhu, H., Meng, T., Li, K., Huang, N., Zhang, S.,
A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images,
MedImg(39), No. 8, August 2020, pp. 2653-2663.
IEEE DOI 2008
Noise measurement, Image segmentation, Lesions, Lung, Training, COVID-19, COVID-19, convolutional neural network, noisy label, pneumonia BibRef

Xie, W., Jacobs, C., Charbonnier, J., van Ginneken, B.,
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans,
MedImg(39), No. 8, August 2020, pp. 2664-2675.
IEEE DOI 2008
Computed tomography, Lung, Image segmentation, Diseases, Convolution, Neural networks, Training, COVID-19, Computed Tomography, COVID-19, Segmentation BibRef

Roy, S., Menapace, W., Oei, S., Luijten, B., Fini, E., Saltori, C., Huijben, I., Chennakeshava, N., Mento, F., Sentelli, A., Peschiera, E., Trevisan, R., Maschietto, G., Torri, E., Inchingolo, R., Smargiassi, A., Soldati, G., Rota, P., Passerini, A., van Sloun, R.J.G., Ricci, E., Demi, L.,
Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound,
MedImg(39), No. 8, August 2020, pp. 2676-2687.
IEEE DOI 2008
Image segmentation, Lung, Ultrasonic imaging, Task analysis, Pathology, Imaging, Diseases, COVID-19, COVID-19, lung ultrasound, deep learning BibRef

Oh, Y., Park, S., Ye, J.C.,
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets,
MedImg(39), No. 8, August 2020, pp. 2688-2700.
IEEE DOI 2008
Lung, Diseases, Image segmentation, Training, Neural networks, Sensitivity, Computed tomography, COVID-19, COVID-19, chest X-ray, saliency map BibRef

Seghier, M.L.[Mohamed L.],
The COVID-19 pandemic: What can bioengineers, computer scientists and big data specialists bring to the table,
IJIST(30), No. 3, 2020, pp. 511-512.
DOI Link 2008
BibRef

Han, Z., Wei, B., Hong, Y., Li, T., Cong, J., Zhu, X., Wei, H., Zhang, W.,
Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning,
MedImg(39), No. 8, August 2020, pp. 2584-2594.
IEEE DOI 2008
Computed tomography, Diseases, Lung, Manuals, Medical diagnostic imaging, machine learning BibRef

Wang, J., Bao, Y., Wen, Y., Lu, H., Luo, H., Xiang, Y., Li, X., Liu, C., Qian, D.,
Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images,
MedImg(39), No. 8, August 2020, pp. 2572-2583.
IEEE DOI 2008
Lung, Diseases, Computed tomography, Lesions, Task analysis, Image segmentation, Biomedical imaging, COVID-19, COVID-19, deep attention learning BibRef

Farhat, H.[Hanan], Sakr, G.E.[George E.], Kilany, R.[Rima],
Deep learning applications in pulmonary medical imaging: Recent updates and insights on COVID-19,
MVA(31), No. 6, August 2020, pp. Article53.
WWW Link. 2008
BibRef

Afshar, P.[Parnian], Heidarian, S.[Shahin], Naderkhani, F.[Farnoosh], Oikonomou, A.[Anastasia], Plataniotis, K.N.[Konstantinos N.], Mohammadi, A.[Arash],
COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images,
PRL(138), 2020, pp. 638-643.
Elsevier DOI 1806
COVID-19 pandemic, X-ray images, Deep learning, Capsule network BibRef


Gabruseva, T., Poplavskiy, D., Kalinin, A.,
Deep Learning for Automatic Pneumonia Detection,
WiCV20(1436-1443)
IEEE DOI 2008
Lung, Diseases, Training, X-ray imaging, Diagnostic radiography, Measurement, Predictive models BibRef

Sousa, G.G.B.[Gabriel Garcez Barros], Fernandes, V.R.M.[Vandécia Rejane Monteiro], de Paiva, A.C.[Anselmo Cardoso],
Optimized Deep Learning Architecture for the Diagnosis of Pneumonia Through Chest X-Rays,
ICIAR19(II:353-361).
Springer DOI 1909
BibRef

Murray, V.[Victor], Pattichis, M.S.[Marios S.], Davis, H.[Herbert], Barriga, E.S.[Eduardo S.], Soliz, P.[Peter],
Multiscale AM-FM analysis of pneumoconiosis x-ray images,
ICIP09(4201-4204).
IEEE DOI 0911
BibRef

Tong, X.[Xiaoou], Tao, D.C.[Da-Cheng], Antonio, G.E.,
Texture classification of SARS infected region in radiographic image,
ICIP04(V: 2941-2944).
IEEE DOI 0505
BibRef

Pattichis, M.S., Pattichis, C.S., Christodoulou, C.I., James, D., Ketai, L., Soliz, P.,
A screening system for the assessment of opacity profusion in chest radiographs of miners with pneumoconiosis,
Southwest02(130-133).
IEEE Top Reference. 0208
BibRef

Chen, X.[Xuan], Hasegawa, J.I., Toriwaki, J.I.,
Quantitative diagnosis of pneumoconiosis based on recognition of small rounded opacities in chest X-ray images,
ICPR88(I: 462-464).
IEEE DOI 8811
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Emphysema, Lung Analysis .


Last update:Oct 19, 2020 at 15:02:28