21.7.3.7 Thorax, Thoracic Analysis

Herrmann, J., Hoffman, E.A., Kaczka, D.W.,
Frequency-Selective Computed Tomography: Applications During Periodic Thoracic Motion,
MedImg(36), No. 8, August 2017, pp. 1722-1732.
IEEE DOI 1708
Computed tomography, Image reconstruction, Image resolution, Motion artifacts, Standards, Ventilation, X-ray imaging, Heart, X-ray imaging and computed tomography, image acquisition, image reconstruction: analytical methods, lung, motion compensation and analysis, tracking, (time, series, analysis) BibRef

Wang, J., Schaffert, R., Borsdorf, A., Heigl, B., Huang, X., Hornegger, J., Maier, A.,
Dynamic 2-D/3-D Rigid Registration Framework Using Point-To-Plane Correspondence Model,
MedImg(36), No. 9, September 2017, pp. 1939-1954.
IEEE DOI 1709
computerised tomography, image registration, point-to-plane correspondence model, single-view X-ray image, thorax phantom, Cameras, Generators, Motion compensation, Robustness, Solid modeling, BibRef

Schaffert, R., Wang, J., Fischer, P., Maier, A., Borsdorf, A.,
Robust Multi-View 2-D/3-D Registration Using Point-To-Plane Correspondence Model,
MedImg(39), No. 1, January 2020, pp. 161-174.
IEEE DOI 2001
Robustness, X-ray imaging, Cameras, Motion estimation, Solid modeling, Feature extraction, Generators, spine registration BibRef

Schaffert, R., Wang, J., Fischer, P., Borsdorf, A., Maier, A.,
Learning an Attention Model for Robust 2-D/3-D Registration Using Point-To-Plane Correspondences,
MedImg(39), No. 10, October 2020, pp. 3159-3174.
IEEE DOI 2010
Robustness, Training, X-ray imaging, Motion estimation, Linear programming, Optimization, Training data, head registration BibRef

Samaghcheh, Z.N.[Zeinab Naseri], Abdoli, F.[Fatemeh], Abrishami Moghaddam, H.[Hamid], Modaresi, M.[Mohammadreza], Pak, N.[Neda],
A new model-based framework for lung tissue segmentation in three-dimensional thoracic CT images,
SIViP(12), No. 2, February 2018, pp. 339-346.
Springer DOI 1802
BibRef

Commandeur, F., Goeller, M., Betancur, J., Cadet, S., Doris, M., Chen, X., Berman, D.S., Slomka, P.J., Tamarappoo, B.K., Dey, D.,
Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT,
MedImg(37), No. 8, August 2018, pp. 1835-1846.
IEEE DOI 1808
Computed tomography, Heart, Calcium, Biomedical imaging, Machine learning, Arteries, Feature extraction, non-contrast computed tomography (CT) BibRef

Price, H.B., Kimbell, J.S., Bu, R., Oldenburg, A.L.,
Geometric Validation of Continuous, Finely Sampled 3-D Reconstructions From aOCT and CT in Upper Airway Models,
MedImg(38), No. 4, April 2019, pp. 1005-1015.
IEEE DOI 1904
Computed tomography, Atmospheric modeling, Phantoms, Image segmentation, Electron tubes, computed tomography BibRef

Yang, M.L.[Meng-Lin], Li, D.[Ding], Zhang, W.S.[Wen-Sheng],
Spatial non-local attention for thoracic disease diagnosis and visualisation in weakly supervised learning,
IET-IPR(13), No. 11, 19 September 2019, pp. 1922-1930.
DOI Link 1909
BibRef

Guan, Q.J.[Qing-Ji], Huang, Y.P.[Ya-Ping], Zhong, Z.[Zhun], Zheng, Z.D.[Zhe-Dong], Zheng, L.[Liang], Yang, Y.[Yi],
Thorax Disease Classification with Attention Guided Convolutional Neural Network,
PRL(131), 2020, pp. 38-45.
Elsevier DOI 2004
CXR image classification, Visual attention, Feature ensemble BibRef

Guan, Q., Huang, Y., Luo, Y., Liu, P., Xu, M., Yang, Y.,
Discriminative Feature Learning for Thorax Disease Classification in Chest X-ray Images,
IP(30), 2021, pp. 2476-2487.
IEEE DOI 2102
Diseases, X-ray imaging, Lesions, Pathology, Medical diagnostic imaging, Location awareness, Semantics, spatial-and-channel encoding BibRef

Zhou, Y., Zhou, T., Zhou, T., Fu, H., Liu, J., Shao, L.,
Contrast-Attentive Thoracic Disease Recognition With Dual-Weighting Graph Reasoning,
MedImg(40), No. 4, April 2021, pp. 1196-1206.
IEEE DOI 2104
Lung, Diseases, X-rays, Visualization, Radiology, Medical diagnosis, Lesions, Thoracic diseases, dual-weighting graph reasoning BibRef

Lian, J.[Jie], Liu, J.Y.[Jing-Yu], Zhang, S.[Shu], Gao, K.[Kai], Liu, X.Q.[Xiao-Qing], Zhang, D.[Dingwen], Yu, Y.Z.[Yi-Zhou],
A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation,
MedImg(40), No. 8, August 2021, pp. 2042-2052.
IEEE DOI 2108
BibRef
And: Correction: MedImg(43), No. 2, February 2024, pp. 899-899.
IEEE DOI 2402
Diseases, Lung, X-ray imaging, Image segmentation, Proposals, Head, Feature extraction, ChestX-Det BibRef

Zhao, G.M.[Gang-Ming], Fang, C.W.[Chao-Wei], Li, G.B.[Guan-Bin], Jiao, L.C.[Li-Cheng], Yu, Y.Z.[Yi-Zhou],
Contralaterally Enhanced Networks for Thoracic Disease Detection,
MedImg(40), No. 9, September 2021, pp. 2428-2438.
IEEE DOI 2109
Diseases, Proposals, X-rays, Feature extraction, Location awareness, Lung, Training, Chest X-ray, disease detection, deep learning BibRef

Hansen, L.[Lasse], Heinrich, M.P.[Mattias P.],
GraphRegNet: Deep Graph Regularisation Networks on Sparse Keypoints for Dense Registration of 3D Lung CTs,
MedImg(40), No. 9, September 2021, pp. 2246-2257.
IEEE DOI 2109
Lung, Computed tomography, Biomedical imaging, Feature extraction, Strain, Estimation, thoracic CT BibRef

Roser, P.[Philipp], Birkhold, A.[Annette], Preuhs, A.[Alexander], Syben, C.[Christopher], Felsner, L.[Lina], Hoppe, E.[Elisabeth], Strobel, N.[Norbert], Kowarschik, M.[Markus], Fahrig, R.[Rebecca], Maier, A.[Andreas],
X-Ray Scatter Estimation Using Deep Splines,
MedImg(40), No. 9, September 2021, pp. 2272-2283.
IEEE DOI 2109
Splines (mathematics), X-ray imaging, Photonics, Detectors, Neural networks, Thorax, Estimation, Approximation, B-spline, X-ray scatter BibRef

Jiang, J.[Jue], Veeraraghavan, H.[Harini],
One Shot PACS: Patient Specific Anatomic Context and Shape Prior Aware Recurrent Registration-Segmentation of Longitudinal Thoracic Cone Beam CTs,
MedImg(41), No. 8, August 2022, pp. 2021-2032.
IEEE DOI 2208
Image segmentation, Shape, Tumors, Strain, Training, Deformable models, Computed tomography, One-shot learning, anatomic context and shape prior BibRef

Zhang, R.H.[Rui-Hua], Yang, F.[Fan], Luo, Y.[Yan], Liu, J.Y.[Jian-Yi], Wang, C.[Cong],
Learning invariant representation for unsupervised domain adaptive thorax disease classification,
PRL(160), 2022, pp. 155-162.
Elsevier DOI 2208
Thorax disease classification, Invariant representation, Unsupervised domain adaptation BibRef

Lee, M.S.[Min Seok], Han, S.W.[Sung Won],
DuETNet: Dual Encoder based Transfer Network for thoracic disease classification,
PRL(161), 2022, pp. 143-153.
Elsevier DOI 2209
Thoracic disease classification, Imbalanced multi-class classification, Attention mechanism BibRef

Zhao, G.Z.[Guang-Zhe], Shao, S.[Shuai], Yu, M.[Min],
Key techniques for classification of thorax diseases based on deep learning,
IJIST(32), No. 6, 2022, pp. 2184-2197.
DOI Link 2212
ChestX-ray image analysis, convolution-al neural networks, medical image classification BibRef

Wu, Y.[Yirui], Li, H.[Hao], Feng, X.[Xi], Casanova, A.[Andrea], Abate, A.F.[Andrea F.], Wan, S.H.[Shao-Hua],
GDRL: An interpretable framework for thoracic pathologic prediction,
PRL(165), 2023, pp. 154-160.
Elsevier DOI 2301
Disentangled representation learning, Group-disentangled feature representation, Thoracic pathologic prediction BibRef

Ashok, M.[Malvika], Gupta, A.[Abhishek], Pandey, M.[Mohit],
HCIU: Hybrid clustered inception-based UNET for the automatic segmentation of organs at risk in thoracic computed tomography images,
IJIST(33), No. 6, 2023, pp. 2203-2217.
DOI Link 2311
deep-learning, globalization, K-mean clustering, localization, organs at risk, segmentation BibRef

Gao, Q.[Qing], Chen, Y.Q.[Yong-Quan], Ju, Z.J.[Zhao-Jie],
Oropharynx Visual Detection by Using a Multi-Attention Single-Shot Multibox Detector for Human-Robot Collaborative Oropharynx Sampling,
HMS(53), No. 6, December 2023, pp. 1073-1082.
IEEE DOI 2312
BibRef

Attia, D., Benazza-Benyahia, A.,
Multiclassification of Vocal Folds Disorders from Videos By Spatio-Temporal Deep Features,
ICIP24(3131-3136)
IEEE DOI 2411
Deep learning, Image segmentation, Endoscopes, Transformers, Spatiotemporal phenomena, Videos, vocal fold disorders, 3D deep neural architectures BibRef

Costi, F.[Flavia], Onchis, D.M.[Darian M.], Istin, C.[Codruta], Cozma, G.V.[Gabriel V.],
Explainability-enhanced Neural Network for Thoracic Diagnosis Improvement,
CAIP23(I:35-44).
Springer DOI 2312
BibRef

Xiao, J.F.[Jun-Fei], Bai, Y.T.[Yu-Tong], Yuille, A.L.[Alan L.], Zhou, Z.[Zongwei],
Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification,
WACV23(3577-3589)
IEEE DOI 2302
Scalability, Transfer learning, X-rays, Transformers, Thorax, Convolutional neural networks, Task analysis, Applications: Biomedical/healthcare/medicine BibRef

Lambert, Z.[Zoé], Petitjean, C.[Caroline], Dubray, B.[Bernard], Kuan, S.[Su],
SegTHOR: Segmentation of Thoracic Organs at Risk in CT images,
IPTA20(1-6)
IEEE DOI 2206
Training, Image segmentation, Computed tomography, Tools, Thorax, Tumors, Image segmentation, CT imaging, organ at risk, dataset, radiotherapy BibRef

Mahmood, H.[Hassan], Islam, S.M.S.[Syed Mohammed Shamsul], Hill, J.[James], Tay, G.[Guan],
Rapid Segmentation of Thoracic Organs using U-net Architecture,
DICTA21(01-06)
IEEE DOI 2201
Image segmentation, Computed tomography, Computational modeling, Computer architecture, Throughput, Planning, U-net, Thorax, Organs, Medical Imaging BibRef

Ma, Y., Ma, A.J., Pan, Y., Chen, X.,
Multi-Scale Feature Pyramids for Weakly Supervised Thoracic Disease Localization,
ICIP20(2481-2485)
IEEE DOI 2011
Diseases, X-ray imaging, Space heating, Task analysis, Feature extraction, Cogeneration, Weakly Supervised Localization, Chest X-ray Image BibRef

Solovyev, R.[Roman], Melekhov, I.[Iaroslav], Lesonen, T.[Timo], Vaattovaara, E.[Elias], Tervonen, O.[Osmo], Tiulpin, A.[Aleksei],
Bayesian Feature Pyramid Networks for Automatic Multi-label Segmentation of Chest X-rays and Assessment of Cardio-thoratic Ratio,
ACIVS20(117-130).
Springer DOI 2003
BibRef

Sze-To, A.[Antonio], Wang, Z.[Zihe],
tCheXNet: Detecting Pneumothorax on Chest X-Ray Images Using Deep Transfer Learning,
ICIAR19(II:325-332).
Springer DOI 1909
BibRef

Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L., Fei-Fei, L.,
Thoracic Disease Identification and Localization with Limited Supervision,
CVPR18(8290-8299)
IEEE DOI 1812
Diseases, Task analysis, X-ray imaging, Predictive models, Biomedical imaging, Interpolation, Image analysis BibRef

Wang, X., Peng, Y., Lu, L., Lu, Z., Summers, R.M.,
TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays,
CVPR18(9049-9058)
IEEE DOI 1812
X-rays, Diseases, Task analysis, Visualization, Biomedical imaging, Training, Feature extraction BibRef

Kumar, P.[Pulkit], Grewal, M.[Monika], Srivastava, M.M.[Muktabh Mayank],
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs,
ICIAR18(546-552).
Springer DOI 1807
BibRef

Ben Aicha, A., Ezzine, K.,
Cancer larynx detection using glottal flow parameters and statistical tools,
ISIVC16(65-70)
IEEE DOI 1704
Cancer BibRef

Novozamsky, A.[Adam], Sedlar, J.[Jiri], Zita, A.[Ales], Sroubek, F.[Filip], Flussef, J.[Jan], Svec, J.G.[Jan G.], Vydrova, J.[Jitka], Zitova, B.[Barbara],
Image analysis of videokymographic data,
ICIP15(78-82)
IEEE DOI 1512
data segmentation; medical imaging; videokymography. laryngology and phoniatrics for examination of vocal fold vibrations. BibRef

Balacey, H.[Hugo], Dournes, G.[Gael], Desbarats, P.[Pascal], Montaudon, M.[Michel], Domenger, J.P.[Jean-Philippe], Laurent, F.[Francois],
A new processing sequence to assess airways using 3D CT-scan,
ICIP13(2339-2343)
IEEE DOI 1402
Biomedical image processing; anatomical structures; image segmentation BibRef

Ivanovska, T.[Tatyana], Dober, J.[Johannes], Laqua, R.[René], Hegenscheid, K.[Katrin], Völzke, H.[Henry],
Pharynx Segmentation from MRI Data for Analysis of Sleep Related Disoders,
ISVC13(I:20-29).
Springer DOI 1310
BibRef

Martins, A.L.D.[Ana L. D.], Mascarenhas, N.D.A.[Nelson D.A.],
Wiener based spatial resolution enhancement of MRI sequences of the vocal tract: A comparison between two correlation models,
ICIP12(869-872).
IEEE DOI 1302
BibRef

Schnurrer, W.[Wolfgang], Seiler, J.[Jurgen], Wige, E.[Eugen], Kaup, A.[Andre],
Analysis of displacement compensation methods for wavelet lifting of medical 3-D thorax CT volume data,
VCIP12(1-6).
IEEE DOI 1302
BibRef

Takizawa, H.[Hotaka], Ishii, S.[Shigeyuki],
Lung Cancer Detection from Thoracic CT Scans Using 3-D Deformable Models Based on Statistical Anatomical Analysis,
MIRAGE11(28-39).
Springer DOI 1110
BibRef

Windisch, L.[Luke], Cheriet, F.[Farida], Grimard, G.[Guy],
Bayesian Differentiation of Multi-scale Line-Structures for Model-Free Instrument Segmentation in Thoracoscopic Images,
ICIAR05(938-948).
Springer DOI 0509
BibRef

Urschler, M.[Martin], Bauer, J.[Joachim], Ditt, H.[Hendrik], Bischof, H.[Horst],
SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images,
CVAMIA06(73-84).
Springer DOI 0605
BibRef

Summers, R., Mullick, R., Finkelstein, S., Schrump, D.,
Confocal Volume Rendering of the Thorax,
ICIP01(II: 297-299).
IEEE DOI 0108
BibRef

Coutand, F., Garnero, L., Fonroget, J.,
Anatomical data fusion for quantitative reconstruction in cardiac tomoscintigraphy using active contours of the organs of the thorax,
ICIP96(II: 733-736).
IEEE DOI 9610
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Pulmonary Nodules, Lung Nodules .

Last update:Nov 26, 2024 at 16:40:19