20.7.2.1 Chest X-Ray Analysis

Chapter Contents (Back)
X Ray. Chest X-Ray. 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

Gradl, R., Morgan, K.S., Dierolf, M., Jud, C., Hehn, L., Günther, B., Möller, W., Kutschke, D., Yang, L., Stoeger, T., Pfeiffer, D., Gleich, B., Achterhold, K., Schmid, O., Pfeiffer, F.,
Dynamic In Vivo Chest X-ray Dark-Field Imaging in Mice,
MedImg(38), No. 2, February 2019, pp. 649-656.
IEEE DOI 1902
Lung, Gratings, Imaging, X-ray imaging, Diseases, Mice, Animal imaging, dark-field and phase-contrast X-ray methods, X-ray imaging 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

Nishio, M.[Mizuho], Fujimoto, K.[Koji], Togashi, K.[Kaori],
Lung segmentation on chest X-ray images in patients with severe abnormal findings using deep learning,
IJIST(31), No. 2, 2021, pp. 1002-1008.
DOI Link 2105
Bayesian optimization, chest X-ray images, dice similarity coefficient, lung segmentation, U-net BibRef

Chen, B.Z.[Bing-Zhi], Zhang, Z.[Zheng], Li, Y.[Yingjian], Lu, G.M.[Guang-Ming], Zhang, D.[David],
Multi-Label Chest X-Ray Image Classification via Semantic Similarity Graph Embedding,
CirSysVideo(32), No. 4, April 2022, pp. 2455-2468.
IEEE DOI 2204
Semantics, Feature extraction, Visualization, Correlation, X-ray imaging, Pathology, Lesions, teacher-student BibRef

Rahman, M.F.[Md Fashiar], Zhuang, Y.[Yan], Tseng, T.L.(.[Tzu-Liang (Bill)], Pokojovy, M.[Michael], McCaffrey, P.[Peter], Walser, E.[Eric], Moen, S.[Scott], Vo, A.[Alex],
Improving lung region segmentation accuracy in chest X-ray images using a two-model deep learning ensemble approach,
JVCIR(85), 2022, pp. 103521.
Elsevier DOI 2205
Lung segmentation, Chest X-ray, Deep learning, UNet, CNNs, Ensemble BibRef

Mao, C.S.[Cheng-Sheng], Yao, L.[Liang], Luo, Y.[Yuan],
ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification With Chest X-Rays,
MedImg(41), No. 8, August 2022, pp. 1990-2003.
IEEE DOI 2208
Image representation, Diseases, X-ray imaging, Biomedical imaging, Image edge detection, Task analysis, Message passing, Chest X-ray, relation modeling BibRef


Guarrasi, V.[Valerio], Soda, P.[Paolo],
Optimized Fusion of CNNs to Diagnose Pulmonary Diseases on Chest X-Rays,
CIAP22(I:197-209).
Springer DOI 2205
BibRef

Mata, D.[Diogo], Silva, W.[Wilson], Cardoso, J.S.[Jaime S.],
Increased Robustness in Chest X-Ray Classification Through Clinical Report-Driven Regularization,
IbPRIA22(119-128).
Springer DOI 2205
BibRef

Han, Y.[Yan], Chen, C.Y.[Chong-Yan], Tewfik, A.[Ahmed], Glicksberg, B.[Benjamin], Ding, Y.[Ying], Peng, Y.[Yifan], Wang, Z.Y.[Zhang-Yang],
Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop,
WACV22(1789-1798)
IEEE DOI 2202
Location awareness, Feedback loop, Annotations, Feature extraction, Data models, Task analysis, X-ray imaging, Semi- and Un- supervised Learning BibRef

Tran, T.T.[Thanh T.], Pham, H.H.[Hieu H.], Nguyen, T.V.[Thang V.], Le, T.T.[Tung T.], Nguyen, H.T.[Hieu T.], Nguyen, H.Q.[Ha Q.],
Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks,
CVAMD21(3307-3316)
IEEE DOI 2112
Deep learning, Training, Pathology, Sensitivity, Lung, Convolutional neural networks, Task analysis BibRef

Monajatipoor, M.[Masoud], Rouhsedaghat, M.[Mozhdeh], Li, L.H.[Liunian Harold], Chien, A.[Aichi], Kuo, C.C.J.[C.C. Jay], Scalzo, F.[Fabien], Chang, K.W.[Kai-Wei],
BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis,
CVAMD21(3327-3336)
IEEE DOI 2112
Visualization, Transformers, Knowledge discovery, Data models, Medical diagnosis BibRef

Kim, E.[Eunji], Kim, S.[Siwon], Seo, M.J.[Min-Ji], Yoon, S.[Sungroh],
XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations,
CVPR21(15714-15723)
IEEE DOI 2111
Location awareness, Deep learning, Computational modeling, Prototypes, Pattern recognition, Diagnostic radiography 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

Reiß, S.[Simon], Seibold, C.[Constantin], Freytag, A.[Alexander], Rodner, E.[Erik], Stiefelhagen, R.[Rainer],
Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation,
CVPR21(9527-9537)
IEEE DOI 2111
Training, Image segmentation, Biomedical equipment, Annotations, Mission critical systems, Medical services, Retina 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

Rashid, R.[Rabia], Akram, M.U.[Muhammad Usman], Hassan, T.[Taimur],
Fully Convolutional Neural Network for Lungs Segmentation from Chest X-Rays,
ICIAR18(71-80).
Springer DOI 1807
BibRef

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.,
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases,
CVPR17(3462-3471)
IEEE DOI 1711
Biomedical imaging, Databases, Diseases, Image segmentation, Machine learning, Pathology, X-ray, imaging 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

Ngo, T.A.[Tuan Anh], Carneiro, G.[Gustavo],
Lung segmentation in chest radiographs using distance regularized level set and deep-structured learning and inference,
ICIP15(2140-2143)
IEEE DOI 1512
Deep learning; Level set methods; Lung segmentation BibRef

Farag, A.[Amal], Graham, J.[James], Farag, A.A.[Aly A.],
Robust segmentation of lung tissue in chest CT scanning,
ICIP10(2249-2252).
IEEE DOI 1009
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

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Lung Motion Analysis, Respiration, Breathing .


Last update:Dec 4, 2022 at 15:58:45