20.7.2.1 Chest X-Ray Analysis

Chapter Contents (Back)
X Ray. Chest. More general analysis. But a lot of overlap with Lung section.

Roellinger, Jr., F.X., Kahveci, A.E., Chang, J.K., Harlow, C.A., Dwyer, III, S.J., Lodwick, G.S.,
Computer Analysis of Chest Radiographs,
CGIP(2), 1973, pp. 232-251. BibRef 7300

Toriwaki, J.I., Suenaga, Y., Negoro, T., Fukumura, T.,
Pattern Recognition of Chest X-Ray Images,
CGIP(2), 1973, pp. 252-271. BibRef 7300

Ballard, D.M., and Sklansky, J.,
A Ladder-Structured Decision Tree for Recognizing Tumors in Chest Radiographs,
TC(25), No. 5, May 1976, pp. 503-513. BibRef 7605

Cocklin, M.L., Gourlay, A.R., Jackson, P.H., Kaye, G., Kerr, I.H., Lams, P.,
Digital Processing of Chest Radiographs,
IVC(1), No. 2, May 1983, pp. 67-78.
Elsevier DOI BibRef 8305

Hasegawa, A., Lo, S.C.B., Lin, J.S., Freedman, M.T., Mun, S.K.,
A Shift-Invariant Neural Network for the Lung Field Segmentation in Chest Radiography,
VLSIVideo(18), No. 3, April 1998, pp. 241-250. 9806
BibRef

van Ginneken, B., ter Haar Romeny, B.M., Viergever, M.A.,
Computer-aided diagnosis in chest radiography: a survey,
MedImg(20), No. 12, December 2001, pp. 1228-1241.
IEEE Top Reference. 0201
Survey, Radiography. BibRef

van Ginneken, B.[Bram], Katsuragawa, S., ter Haar Romeny, B.M., Doi, K.[Kunio], Viergever, M.A.,
Automatic detection of abnormalities in chest radiographs using local texture analysis,
MedImg(21), No. 2, February 2002, pp. 139-149.
IEEE Top Reference. 0204
BibRef

Shi, Y., Qi, F., Xue, Z., Chen, L., Ito, K., Matsuo, H., Shen, D.,
Segmenting Lung Fields in Serial Chest Radiographs Using Both Population-Based and Patient-Specific Shape Statistics,
MedImg(27), No. 4, April 2008, pp. 481-494.
IEEE DOI 0804
BibRef

Philipsen, R.H.H.M., Maduskar, P., Hogeweg, L., Melendez, J., Sanchez, C.I., van Ginneken, B.,
Localized Energy-Based Normalization of Medical Images: Application to Chest Radiography,
MedImg(34), No. 9, September 2015, pp. 1965-1975.
IEEE DOI 1509
Biomedical imaging BibRef

Salehinejad, H., Colak, E., Dowdell, T., Barfett, J., Valaee, S.,
Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks,
MedImg(38), No. 5, May 2019, pp. 1197-1206.
IEEE DOI 1905
X-ray imaging, Computed tomography, Biomedical imaging, Magnetic resonance imaging, Training, synthesized images BibRef

Bozorgtabar, B.[Behzad], Mahapatra, D.[Dwarikanath], von Teng, H.[Hendrik], Pollinger, A.[Alexander], Ebner, L.[Lukas], Thiran, J.P.[Jean-Phillipe], Reyes, M.[Mauricio],
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays,
CVIU(184), 2019, pp. 57-65.
Elsevier DOI 1906
GAN, Active learning, Classification, Chest xray, Informative samples BibRef

Jin, Y.[Yan], Jiang, X.B.[Xiao-Ben], Wei, Z.K.[Zhen-Kun], Li, Y.[Yuan],
Chest X-ray image denoising method based on deep convolution neural network,
IET-IPR(13), No. 11, 19 September 2019, pp. 1970-1978.
DOI Link 1909
BibRef

Guan, Q.J.[Qing-Ji], Huang, Y.P.[Ya-Ping],
Multi-label chest X-ray image classification via category-wise residual attention learning,
PRL(130), 2020, pp. 259-266.
Elsevier DOI 2002
Chest X-ray, Residual attention, Convolutional neural network, Image classification BibRef

Luo, L., Yu, L., Chen, H., Liu, Q., Wang, X., Xu, J., Heng, P.A.,
Deep Mining External Imperfect Data for Chest X-Ray Disease Screening,
MedImg(39), No. 11, November 2020, pp. 3583-3594.
IEEE DOI 2011
Diseases, X-ray imaging, Training, Task analysis, Training data, Biomedical imaging, Predictive models, uncertainty BibRef

Cho, Y.[Yongwon], Lee, S.M.[Sang Min], Cho, Y.H.[Young-Hoon], Lee, J.G.[June-Goo], Park, B.[Beomhee], Lee, G.[Gaeun], Kim, N.[Namkug], Seo, J.B.[Joon Beom],
Deep chest X-ray: Detection and classification of lesions based on deep convolutional neural networks,
IJIST(31), No. 1, 2021, pp. 72-81.
DOI Link 2102
chest radiographs, computer-aided detection, deep learning, lung diseases, machine learning, radiography BibRef

Yang, B.[Bing], Kang, Y.[Yan], Zhang, L.[Lan], Li, H.[Hao],
GGAC: Multi-relational image gated GCN with attention convolutional binary neural tree for identifying disease with chest X-rays,
PR(120), 2021, pp. 108113.
Elsevier DOI 2109
Multi-relational graph, Gated graph convolutional network, Identifying disease, Attention transformer BibRef

Paul, A.[Angshuman], Shen, T.C.[Thomas C.], Lee, S.[Sungwon], Balachandar, N.[Niranjan], Peng, Y.F.[Yi-Fan], Lu, Z.Y.[Zhi-Yong], Summers, R.M.[Ronald M.],
Generalized Zero-Shot Chest X-Ray Diagnosis Through Trait-Guided Multi-View Semantic Embedding With Self-Training,
MedImg(40), No. 10, October 2021, pp. 2642-2655.
IEEE DOI 2110
Semantics, X-ray imaging, Computed tomography, Radiology, Visualization, Diseases, Training, Multi-view, self-training, x-ray, zero-shot BibRef

Ouyang, X.[Xi], Karanam, S.[Srikrishna], Wu, Z.[Ziyan], Chen, T.[Terrence], Huo, J.[Jiayu], Zhou, X.S.[Xiang Sean], Wang, Q.[Qian], Cheng, J.Z.[Jie-Zhi],
Learning Hierarchical Attention for Weakly-Supervised Chest X-Ray Abnormality Localization and Diagnosis,
MedImg(40), No. 10, October 2021, pp. 2698-2710.
IEEE DOI 2110
Location awareness, Annotations, Task analysis, X-ray imaging, Visualization, Diseases, Image analysis, Weakly supervised, hierarchical attention BibRef

Yin, B.C.[Bao-Cai], Liu, W.C.[Wen-Chao], Fu, Z.H.[Zhong-Hua], Zhang, J.[Jing], Liu, C.[Cong], Wang, Z.F.[Zeng-Fu],
Generative domain adaptation for chest X-ray image analysis,
IET-IPR(15), No. 13, 2021, pp. 3118-3129.
DOI Link 2110
BibRef


Zhang, L.[Lipei], Liu, A.[Aozhi], Xiao, J.[Jing], Taylor, P.[Paul],
Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-ray Segmentation,
ICPR21(9333-9339)
IEEE DOI 2105
Deep learning, Image segmentation, Hospitals, Feature extraction, Data models, Decoding, Convolutional neural networks, DEFU-Net BibRef

Seibold, C.[Constantin], Kleesiek, J.[Jens], Schlemmer, H.P.[Heinz-Peter], Stiefelhagen, R.[Rainer],
Self-guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs,
ACCV20(V:617-634).
Springer DOI 2103
BibRef

Majdi, M.S., Salman, K.N., Morris, M.F., Merchant, N.C., Rodriguez, J.J.,
Deep Learning Classification of Chest X-Ray Images,
SSIAI20(116-119)
IEEE DOI 2009
computerised tomography, diagnostic radiography, diseases, image classification, learning (artificial intelligence), cardiomegaly BibRef

Liu, J., Zhao, G., Fei, Y., Zhang, M., Wang, Y., Yu, Y.,
Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network With Limited Supervision,
ICCV19(10631-10640)
IEEE DOI 2004
diagnostic radiography, diseases, learning (artificial intelligence), medical image processing, Visualization BibRef

Zhou, B.[Bo], Lin, X.[Xunyu], Eck, B.[Brendan], Hou, J.[Jun], Wilson, D.[David],
Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs Using Multi-scale and Conditional Adversarial Network,
ACCV18(I:298-313).
Springer DOI 1906
Dual-energy (DE) chest radiographs. BibRef

Wang, C.L.[Chun-Liang],
Segmentation of Multiple Structures in Chest Radiographs Using Multi-task Fully Convolutional Networks,
SCIA17(II: 282-289).
Springer DOI 1706
BibRef

Wan Ahmad, W.S.H.M.[Wan Siti Halimatul Munirah], Wan Zaki, W.M.D.[Wan Mimi Diyana], Ahmad Fauzi, M.F.[Mohammad Faizal], Tan, W.H.[Wooi Haw],
Classification of Infection and Fluid Regions in Chest X-Ray Images,
DICTA16(1-5)
IEEE DOI 1701
Feature extraction BibRef

Shin, H.C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., Summers, R.M.,
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation,
CVPR16(2497-2506)
IEEE DOI 1612
BibRef

Hirano, Y.S.[Yasu-Shi], Mekada, Y.[Yoshito], Hasegawa, J.I.[Jun-Ichi], Toriwaki, J.I.[Jun-Ichiro],
Quantification of the Spatial Distributionof Line Segments with Applications to CAD of Chest X-Ray CT Images,
WTRCV02(389-412). 0204
BibRef

Ramachandran, J., Pattichis, M.S., Soliz, P.,
Pre-classification of chest radiographs for improved active shape model segmentation of ribs,
Southwest02(188-192).
IEEE Top Reference. 0208
BibRef

Pattichis, M.S., Muralidharan, H., Pattichis, C.S., Soliz, P.,
New image processing models for opacity image analysis in chest radiographs,
Southwest02(260-264).
IEEE Top Reference. 0208
BibRef

Ugurlu, Y.[Yucel], Ohkura, K.[Keiko], Obi, T.[Takashi], Hasegawa, A.[Akira], Yamaguchi, M.[Masahiro], Ohyama, N.[Nagaaki],
Detection of Increasing Profusion of Opacities from a Sequence of Personal Chest Radiographs,
ICIP99(III:402-406).
IEEE DOI BibRef 9900

Hasegawa, J.I.[Jun-Ichi], Hirano, Y.S.[Yasu-Shi], Toriwaki, J.I.[Jun-Ichiro], Ohmatsu, N.[Nobuhiro], Mekada, Y.[Yoshito], Eguchi, K.[Kenji],
Three Dimensional Concentration Index: A Local Feature for Analyzing Three Dimensional Digital Line Patterns and Its Application to Chest X-Ray CT Images,
ICPR98(Vol II: 1040-1043).
IEEE DOI 9808
BibRef

Hara, T., Fujita, H., Lee, Y.B.[Yong-Bum], Yoshimura, H., Kido, S.,
Automated lesion detection methods for 2D and 3D chest X-ray images,
CIAP99(768-773).
IEEE DOI 9909
BibRef

Zhang, Y.Q., Loew, M.H., Pickholtz, R.L.,
On modeling the distribution of chest X-ray images and their stochastic properties,
ICPR90(II: 218-223).
IEEE DOI 9208
BibRef

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


Last update:Oct 16, 2021 at 11:54:21