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Medical image; Computer-aided diagnosis (CADx); Lung nodules; Getis?
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dynamic lung CT imaging; 3D optic flow
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computerised tomography
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Tennakoon, R.B.[Ruwan B.],
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Training, Medical diagnostic imaging, Task analysis, Training data,
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Brain modeling
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1504
Biomedical imaging
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Accuracy
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Setio, A.A.A.,
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Cancer
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1609
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1612
Biomedical imaging
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Biomedical imaging
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Deep learning
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1701
Lung nodule detection
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Chen, S.,
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Computational modeling
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Farhangi, M.M.,
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3-D Active Contour Segmentation Based on Sparse Linear Combination of
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IEEE DOI
1711
Active contours, Cancer, Image segmentation, Level set, Lungs, Shape,
Adaptive shape prior, X-ray CT,
dictionary learning, level set segmentation, lung nodules, sparse, representation
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Zhang, W.,
Song, Y.,
Chen, Y.,
Ma, J.,
Sun, J.,
Zhao, J.,
Limited-Range Few-View CT: Using Historical Images for ROI
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MedImg(36), No. 12, December 2017, pp. 2569-2577.
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1712
Lung nodule follow-up, radiation reduction, the first CT scans
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Liu, X.L.[Xing-Long],
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PR(77), 2018, pp. 262-275.
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Computed tomography, Lung nodule, CNNs
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Li, X.X.[Xiang-Xia],
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IET-IPR(12), No. 7, July 2018, pp. 1253-1264.
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Nodule detection, Convolutional neural network,
False positive reduction, Computer-aided diagnosis
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Hu, Y.,
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Halpenny, D.,
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Veeraraghavan, H.,
Multiple Resolution Residually Connected Feature Streams for
Automatic Lung Tumor Segmentation From CT Images,
MedImg(38), No. 1, January 2019, pp. 134-144.
IEEE DOI
1901
Image resolution, Tumors, Streaming media, Lung, Cancer,
Feature extraction, Image segmentation, Deep learning,
detection
BibRef
Gerard, S.E.,
Patton, T.J.,
Christensen, G.E.,
Bayouth, J.E.,
Reinhardt, J.M.,
FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection
in CT Images,
MedImg(38), No. 1, January 2019, pp. 156-166.
IEEE DOI
1901
Lung, Computed tomography, Image segmentation, Feature extraction,
Detectors, Machine learning, Training, Lung, segmentation,
CNN
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Huidrom, R.[Ratishchandra],
Chanu, Y.J.[Yambem Jina],
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Pulmonary nodule detection on computed tomography using
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SIViP(13), No. 1, February 2019, pp. 53-60.
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1901
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Zhang, Z.C.[Zhan-Cheng],
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Xie, Y.,
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Cai, W.,
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung
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MedImg(38), No. 4, April 2019, pp. 991-1004.
IEEE DOI
1904
Lung, Cancer, Feature extraction, Computed tomography,
Shape, Machine learning,
computed tomography (CT)
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Moghaddam, A.E.[Amal Eisapour],
Akbarizadeh, G.[Gholamreza],
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Automatic detection and segmentation of blood vessels and pulmonary
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SIViP(13), No. 3, April 2019, pp. 457-464.
Springer DOI
1904
BibRef
Roy, R.[Rukhmini],
Chakraborti, T.[Tapabrata],
Chowdhury, A.S.[Ananda S.],
A deep learning-shape driven level set synergism for pulmonary nodule
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Elsevier DOI
1906
Lung nodule segmentation, Level sets, Shape information,
Convolutional neural networks
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Roy, R.[Rukhmini],
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A Level Set Based Unified Framework for Pulmonary Nodule Segmentation,
SPLetters(27), 2020, pp. 1465-1469.
IEEE DOI
2009
Level set, Shape, Lung, Solids, Image segmentation, Glass,
Machine learning, Pulmonary nodule segmentation, level sets,
contrast adaptation
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Lung tumour detection by fusing extended local binary patterns and
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IET-IPR(13), No. 6, 10 May 2019, pp. 877-884.
DOI Link
1906
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Theodorakis, L.,
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Prassopoulos, V.,
Kappas, C.,
Tsougos, I.,
Georgoulias, P.,
PET Counting Response Variability Depending on Tumor Location,
Activity, and Patient Obesity: A Feasibility Study of Solitary
Pulmonary Nodule Using Monte Carlo,
MedImg(38), No. 7, July 2019, pp. 1763-1774.
IEEE DOI
1907
Tumors, Detectors, Biological system modeling, Data models,
Positron emission tomography, Obesity, Biographies,
nuclear imaging
BibRef
Sathiya, T.[Thanikachalam],
Sathiyabhama, B.[Balasubramaniam],
Fuzzy relevance vector machine based classification of lung nodules in
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IJIST(29), No. 3, September 2019, pp. 360-373.
DOI Link
1908
BibRef
Paulraj, T.[Tharcis],
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Lung computed axial tomography image segmentation using possibilistic
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IJIST(29), No. 3, September 2019, pp. 374-381.
DOI Link
1908
BibRef
Zheng, S.,
Guo, J.,
Cui, X.,
Veldhuis, R.N.J.,
Oudkerk, M.,
van Ooijen, P.M.A.,
Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional
Neural Networks Based on Maximum Intensity Projection,
MedImg(39), No. 3, March 2020, pp. 797-805.
IEEE DOI
2004
Maximum intensity projection (MIP),
convolutional neural network (CNN),
computed tomography scan
BibRef
Liu, L.,
Dou, Q.,
Chen, H.,
Qin, J.,
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Lung nodule, benign-malignant diagnosis,
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computed tomography, computer-aided detection,
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convolutional neural networks, false positive reduction,
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Rani, K.V.[K. Vijila],
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Superpixel with nanoscale imaging and boosted deep convolutional
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2011
artificial neural network, ATMSBR segmentation,
bag of visual words classifier, nanoimaging
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Rani, K.V.[K. Vijila],
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Liu, S.,
Setio, A.A.A.,
Ghesu, F.C.,
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IEEE DOI
2012
Training, Robustness, Lung, Cancer, Computed tomography,
Biomedical imaging, Benchmark testing, Lung nodule detection,
deep learning
BibRef
Jiang, H.L.[Han-Liang],
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Gao, F.[Fei],
Han, W.D.[Wei-Dong],
Learning efficient, explainable and discriminative representations
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PR(113), 2021, pp. 107825.
Elsevier DOI
2103
Pulmonary nodule classification, Convolutional neural network,
Neural architecture search, Computer-aided diagnoses,
Convolutional block attention module
BibRef
Bu, T.[Tian],
Yang, Z.Y.[Zhi-Yong],
Jiang, S.[Shan],
Zhang, G.B.[Guo-Bin],
Zhang, H.Y.[Hong-Yun],
Wei, L.[Lin],
3D conditional generative adversarial network-based synthetic medical
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IJIST(31), No. 2, 2021, pp. 670-681.
DOI Link
2105
3D squeeze-and-excitation network, computer-aided detection,
conditional generative adversarial network,
lung nodules
BibRef
Afshar, P.[Parnian],
Naderkhani, F.[Farnoosh],
Oikonomou, A.[Anastasia],
Rafiee, M.J.[Moezedin Javad],
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Plataniotis, K.N.[Konstantinos N.],
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Elsevier DOI
2106
Tumor type classification, Capsule network, Mixture of experts
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Hernández-Solis, V.[Vicente],
Téllez-Velázquez, A.[Arturo],
Orantes-Molina, A.[Antonio],
Cruz-Barbosa, R.[Raúl],
Lung-Nodule Segmentation Using a Convolutional Neural Network with the
U-Net Architecture,
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Springer DOI
2108
BibRef
Sahu, S.P.[Satya Prakash],
Londhe, N.D.[Narendra D.],
Verma, S.[Shrish],
Singh, B.K.[Bikesh K.],
Banchhor, S.K.[Sumit Kumar],
Improved pulmonary lung nodules risk stratification in computed
tomography images by fusing shape and texture features in a
machine-learning paradigm,
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DOI Link
2108
computed tomography, computer-aided diagnosis system,
feature extraction, feature selection, nodule segmentation,
support vector machine
BibRef
Khomkham, B.[Banphatree],
Lipikorn, R.[Rajalida],
Pulmonary lesion classification from endobronchial ultrasonography
images using adaptive weighted-sum of the upper and lower triangular
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IJIST(31), No. 3, 2021, pp. 1280-1293.
DOI Link
2108
adaptive weighted-sum of the lower triangular gray-level co-occurrence matrix,
support vector machine
BibRef
Wang, Q.L.[Qiu-Li],
Zhang, X.H.[Xiao-Hong],
Zhang, W.[Wei],
Gao, M.C.[Ming-Chen],
Huang, S.[Sheng],
Wang, J.[Jian],
Zhang, J.Q.[Jiu-Quan],
Yang, D.[Dan],
Liu, C.[Chen],
Realistic Lung Nodule Synthesis With Multi-Target Co-Guided
Adversarial Mechanism,
MedImg(40), No. 9, September 2021, pp. 2343-2353.
IEEE DOI
2109
BibRef
And:
Erratum:
MedImg(40), No. 12, December 2021, pp. 3969-3969.
IEEE DOI
2112
Lung, Semantics, Shape, Computed tomography,
Generative adversarial networks, Biomedical imaging,
adversarial learning
BibRef
Mobiny, A.[Aryan],
Yuan, P.Y.[Peng-Yu],
Cicalese, P.A.[Pietro A.],
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Wu, C.C.[Carol C.],
Wong, K.[Kelvin],
Wong, S.T.[Stephen T.],
He, T.C.[Tian Cheng],
Nguyen, H.V.[Hien V.],
Memory-Augmented Capsule Network for Adaptable Lung Nodule
Classification,
MedImg(40), No. 10, October 2021, pp. 2869-2879.
IEEE DOI
2110
Lung, Training, Task analysis, Feature extraction, Adaptation models,
Data models, Computed tomography, Capsule network, meta-learning
BibRef
Balagurunathan, Y.[Yoganand],
Beers, A.[Andrew],
Mcnitt-Gray, M.[Michael],
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Kapur, T.[Tina],
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Moon, J.W.[Jung Won],
Perez, G.B.[Gabriel Bernardino],
Delgado-Gonzalo, R.[Ricard],
Farhangi, M.M.[M. Mehdi],
Amini, A.A.[Amir A.],
Ni, R.[Renkun],
Feng, X.[Xue],
Bagari, A.[Aditya],
Vaidhya, K.[Kiran],
Veasey, B.[Benjamin],
Safta, W.[Wiem],
Frigui, H.[Hichem],
Enguehard, J.[Joseph],
Gholipour, A.[Ali],
Castillo, L.S.[Laura Silvana],
Daza, L.A.[Laura Alexandra],
Pinsky, P.[Paul],
Kalpathy-Cramer, J.[Jayashree],
Farahani, K.[Keyvan],
Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of
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MedImg(40), No. 12, December 2021, pp. 3748-3761.
IEEE DOI
2112
Training, Lung, Deep learning, Lung cancer, Computed tomography,
Biomedical imaging, Pathology, Lung cancer, nodules challenge,
deep learning methods in lung CT
BibRef
Al-Shabi, M.[Mundher],
Shak, K.[Kelvin],
Tan, M.[Maxine],
ProCAN: Progressive growing channel attentive non-local network for
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PR(122), 2022, pp. 108309.
Elsevier DOI
2112
Self-Attention, Non-local network, Nodule classification,
Curriculum learning, Deep learning
BibRef
Chen, H.[Hao],
Xia, Y.[Yu],
Duan, H.[Hongbai],
Ban, D.[Duo],
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Qiang, Y.Q.[Yong-Qian],
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IJIST(31), No. 4, 2021, pp. 2283-2294.
DOI Link
2112
CT signs, evolutionary ensemble learning, grade of malignancy,
quantization analysis
BibRef
Zhang, X.F.[Xiao-Fang],
Li, S.[Suxiao],
Zhang, B.[Bin],
Dong, J.[Jie],
Zhao, S.J.[Shu-Jun],
Liu, X.M.[Xiao-Min],
Automatic detection and segmentation of lung nodules in different
locations from CT images based on adaptive a-hull algorithm and
DenseNet convolutional network,
IJIST(31), No. 4, 2021, pp. 1882-1893.
DOI Link
2112
contour correction, CT image, DenseNet, lung nodules, segmentation, a-hull
BibRef
Fu, Y.[Yu],
Xue, P.[Peng],
Zhao, P.[Peng],
Li, N.[Ning],
Xu, Z.[Zhuodong],
Ji, H.[Huizhong],
Zhang, Z.[Zhili],
Cui, W.T.[Wen-Tao],
Dong, E.[Enqing],
3D multi-resolution deep learning model for diagnosis of multiple
pathological types on pulmonary nodules,
IJIST(32), No. 1, 2022, pp. 74-87.
DOI Link
2201
deep learning, multiple pathological types,
multi-resolution method, pulmonary nodules
BibRef
Dodia, S.[Shubham],
Basava, A.[Annappa],
Anand, M.P.[Mahesh Padukudru],
A novel receptive field-regularized V-net and nodule classification
network for lung nodule detection,
IJIST(32), No. 1, 2022, pp. 88-101.
DOI Link
2201
lung cancer, NCNet, nodule detection, pseudo-color, RFR V-net
BibRef
Wang, G.[Guotai],
Zhai, S.[Shuwei],
Lasio, G.[Giovanni],
Zhang, B.[Baoshe],
Yi, B.[Byong],
Chen, S.F.[Shi-Feng],
Macvittie, T.J.[Thomas J.],
Metaxas, D.N.[Dimitris N.],
Zhou, J.H.[Jing-Hao],
Zhang, S.T.[Shao-Ting],
Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis
From Lung CT Scans With Multi-Scale Guided Dense Attention,
MedImg(41), No. 3, March 2022, pp. 531-542.
IEEE DOI
2203
Image segmentation, Lesions, Lung, Computed tomography,
Task analysis, Lung cancer, Semi-supervised learning,
lung CT
BibRef
Fu, X.H.[Xiao-Hang],
Bi, L.[Lei],
Kumar, A.[Ashnil],
Fulham, M.[Michael],
Kim, J.M.[Jin-Man],
An attention-enhanced cross-task network to analyse lung nodule
attributes in CT images,
PR(126), 2022, pp. 108576.
Elsevier DOI
2204
Deep learning, Lung nodule analysis, Multi-task,
Computed tomography (CT), Attention
BibRef
Momoki, Y.[Yohei],
Ichinose, A.[Akimichi],
Shigeto, Y.[Yutaro],
Honda, U.[Ukyo],
Nakamura, K.[Keigo],
Matsumoto, Y.J.[Yu-Ji],
Characterization of Pulmonary Nodules in Computed Tomography Images
Based on Pseudo-Labeling Using Radiology Reports,
CirSysVideo(32), No. 5, May 2022, pp. 2582-2591.
IEEE DOI
2205
Radiology, Lung, Annotations, Training data, Manuals,
Computed tomography, Training, Computer-aided diagnosis,
radiology report
BibRef
Zhang, X.F.[Xiao-Fang],
Zhang, B.[Bin],
Liu, X.M.[Xiao-Min],
Dong, J.[Jie],
Zhao, S.J.[Shu-Jun],
Li, S.[Suxiao],
Accurate classification of nodules and non-nodules from computed
tomography images based on radiomics and machine learning algorithms,
IJIST(32), No. 3, 2022, pp. 956-968.
DOI Link
2205
classification, CT image, lung nodules, machine learning, radiomics
BibRef
Mei, J.[Jie],
Cheng, M.M.[Ming-Ming],
Xu, G.[Gang],
Wan, L.R.[Lan-Ruo],
Zhang, H.[Huan],
SANet: A Slice-Aware Network for Pulmonary Nodule Detection,
PAMI(44), No. 8, August 2022, pp. 4374-4387.
IEEE DOI
2207
Lung, Computed tomography, Feature extraction, Proposals,
Object detection, Pulmonary nodule detection, nodule dataset,
false positive reduction
BibRef
Liao, Z.H.[Ze-Hui],
Xie, Y.T.[Yu-Tong],
Hu, S.S.[Shi-Shuai],
Xia, Y.[Yong],
Learning From Ambiguous Labels for Lung Nodule Malignancy Prediction,
MedImg(41), No. 7, July 2022, pp. 1874-1884.
IEEE DOI
2207
Lung, Reliability, Annotations, Cancer, Computed tomography,
Predictive models, Training, Nodule malignancy prediction,
computed tomography
BibRef
Zhang, Z.[Zhili],
Xiao, T.[Taohui],
Fu, Y.[Yu],
Gao, Y.Q.[Yu-Qiang],
Ren, M.[Meirong],
Cui, W.T.[Wen-Tao],
Dong, E.[Enqing],
3D multi-resolution attention capsule network for diagnosing
multi-pathological types of pulmonary nodules,
IJIST(32), No. 5, 2022, pp. 1727-1742.
DOI Link
2209
attention mechanism, capsule network, deep learning,
multi-pathological classification, pulmonary nodule diagnosis
BibRef
Jeniba, J.S.[J. Sahaya],
Milton, A.,
A multilevel self-attention based segmentation and classification
technique using Directional Hexagonal Mixed Pattern algorithm for
lung nodule detection in thoracic CT image,
IJIST(32), No. 5, 2022, pp. 1496-1510.
DOI Link
2209
classification, Directional Hexagonal Mixed Pattern,
lung nodules, multilevel self-attention and CT images, segmentation
BibRef
Yi, L.[Le],
Zhang, L.[Lei],
Xu, X.Y.[Xiu-Yuan],
Guo, J.X.[Ji-Xiang],
Multi-Label Softmax Networks for Pulmonary Nodule Classification
Using Unbalanced and Dependent Categories,
MedImg(42), No. 1, January 2023, pp. 317-328.
IEEE DOI
2301
Lung, Feature extraction, Task analysis, Morphology,
Computed tomography, Indexes, Lung cancer, Category imbalance,
multi-label classification
BibRef
Liu, W.H.[Wei-Hua],
Liu, X.B.[Xia-Bi],
Luo, X.B.[Xiong-Biao],
Wang, M.R.[Mu-Rong],
Han, G.H.[Guang-Hui],
Zhao, X.M.[Xin-Ming],
Zhu, Z.[Zheng],
A pyramid input augmented multi-scale CNN for GGO detection in 3D
lung CT images,
PR(136), 2023, pp. 109261.
Elsevier DOI
2301
Ground-Glass Opacity nodules.
GGO detection, Multi-scale processing, 3D CT scans, Pyramid inputs
BibRef
Tao, J.[Juliang],
Wang, Y.L.[Yong-Li],
Ding, X.Y.[Xiao-Yun],
A quantitative evaluation of lung nodule spiculation based on image
enhancement,
IET-IPR(17), No. 4, 2023, pp. 1086-1096.
DOI Link
2303
image enhancement, lung nodule segmentation, Random Walk, spiculation
BibRef
Lucas, M.[Mirtha],
Lerma, M.[Miguel],
Furst, J.[Jacob],
Raicu, D.[Daniela],
Explainable Model for Localization of Spiculation in Lung Nodules,
MIA-COVID19D22(457-471).
Springer DOI
2304
BibRef
Wang, Y.[Yiyang],
Qiu, B.[Bowen],
Ramaraj, T.[Thiruvarangan],
Ustun, I.[Ilyas],
Furst, J.[Jacob],
Raicu, D.[Daniela],
Lung Nodule Malignancy Subtype Discovery with Semantic Learning,
ICPR22(4234-4240)
IEEE DOI
2212
Solid modeling, Design automation, Annotations, Image databases,
Semantics, Lung, Lung cancer
BibRef
Fu, X.L.[Xin-Liang],
Zheng, J.Y.[Jia-Yin],
Mai, J.[Juanyun],
Shao, Y.B.[Yan-Bo],
Wang, M.H.[Ming-Hao],
Li, L.[Linyu],
Diao, Z.Q.[Zhao-Qi],
Chen, Y.L.[Yu-Long],
Xiao, J.Y.[Jian-Yu],
You, J.[Jian],
Yin, A.[Airu],
Yang, Y.[Yang],
Qiu, X.C.[Xiang-Cheng],
Tao, J.S.[Jin-Sheng],
Wang, B.[Bo],
Ji, H.[Hua],
A Coarse-to-Fine Morphological Approach with Knowledge-Based Rules
and Self-Adapting Correction for Lung Nodules Segmentation,
ICIP22(1696-1700)
IEEE DOI
2211
Image segmentation, Solid modeling, Thresholding (Imaging),
Automation, Biological system modeling, Knowledge based systems,
self-adapting strategy
BibRef
Chaudhry, H.A.H.[Hafiza Ayesha Hoor],
Renzulli, R.[Riccardo],
Perlo, D.[Daniele],
Santinelli, F.[Francesca],
Tibaldi, S.[Stefano],
Cristiano, C.[Carmen],
Grosso, M.[Marco],
Fiandrotti, A.[Attilio],
Lucenteforte, M.[Maurizio],
Cavagnino, D.[Davide],
Lung Nodules Segmentation with DeepHealth Toolkit,
DeepHealth22(487-497).
Springer DOI
2208
BibRef
Pattisapu, V.K.[Varaha Karthik],
Daunhawer, I.[Imant],
Weikert, T.[Thomas],
Sauter, A.[Alexander],
Stieltjes, B.[Bram],
Vogt, J.E.[Julia E.],
PET-Guided Attention Network for Segmentation of Lung Tumors from
PET/CT Images,
GCPR20(445-458).
Springer DOI
2110
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Chen, W.[Wei],
Wang, Q.[Qiuli],
Wang, K.[Kun],
Yang, D.[Dan],
Zhang, X.H.[Xiao-Hong],
Liu, C.[Chen],
Li, Y.C.[Yu-Cong],
MTGAN: Mask and Texture-driven Generative Adversarial Network for
Lung Nodule Segmentation,
ICPR21(1029-1035)
IEEE DOI
2105
Training, Visualization, Shape, Computed tomography, Lung,
Lung cancer, Benchmark testing
BibRef
Chen, W.[Wei],
Wang, Q.[Qiuli],
Yang, D.[Dan],
Zhang, X.H.[Xiao-Hong],
Liu, C.[Chen],
Li, Y.C.[Yu-Cong],
End-to-End Multi-Task Learning for Lung Nodule Segmentation and
Diagnosis,
ICPR21(6710-6717)
IEEE DOI
2105
Deep learning, Design automation, Lung, Feature extraction,
Pattern recognition, Computer aided diagnosis, Task analysis,
Classification
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Olulana, K.[Kolawole],
Owolawi, P.[Pius],
Tu, C.L.[Chun-Ling],
Abe, B.[Bolanle],
Nodule Generation of Lung CT Images Using a 3d Convolutional LSTM
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ISVC20(II:753-760).
Springer DOI
2103
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Shaffie, A.,
Soliman, A.,
Khalifeh, H.A.,
Ghazal, M.,
Taher, F.,
Elmaghraby, A.,
Keynton, R.,
El-Baz, A.,
A Comprehensive Framework For Accurate Classification of Pulmonary
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ICIP20(408-412)
IEEE DOI
2011
Lung, Cancer, Feature extraction,
Computed tomography, Histograms, Isosurfaces, CAD, SIHOG,
Deep Neural Network.
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Xu, W.,
Wang, K.,
Lin, J.,
Lu, Y.,
Huang, S.,
Zhang, X.,
Knowledge-Guided And Hyper-Attention Aware Joint Network For
Benign-Malignant Lung Nodule Classification,
ICIP20(310-314)
IEEE DOI
2011
Cancer, Feature extraction, Lung, Semantics, Computed tomography,
Visualization, Training, Lung nodule classification,
Priordomain knowledge
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Mejía, C.I.L.[Cecilia Irene Loeza],
Biswal, R.R.,
Rodriguez-Tello, E.[Eduardo],
Ochoa-Ruiz, G.[Gilberto],
Accurate Identification of Tomograms of Lung Nodules Using Cnn:
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Springer DOI
2007
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Kaluva, K.C.[Krishna Chaitanya],
Vaidhya, K.[Kiran],
Chunduru, A.[Abhijith],
Tarai, S.[Sambit],
Nadimpalli, S.P.P.[Sai Prasad Pranav],
Vaidya, S.[Suthirth],
An Automated Workflow for Lung Nodule Follow-up Recommendation Using
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ICIAR20(II:369-377).
Springer DOI
2007
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Katz, O.[Or],
Presil, D.[Dan],
Cohen, L.[Liz],
Schwartzbard, Y.[Yael],
Hoch, S.[Sarah],
Kashani, S.[Shlomo],
Pulmonary-nodule Detection Using an Ensemble of 3d Se-resnet18 and
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ICIAR20(II:378-385).
Springer DOI
2007
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Tian, Z.H.[Zhen-Huan],
Jia, Y.Z.[Yi-Zhuan],
Men, X.J.[Xue-Jun],
Sun, Z.W.[Zhong-Wei],
3dcnn for Pulmonary Nodule Segmentation and Classification,
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Springer DOI
2007
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Galdran, A.[Adrian],
Bouchachia, H.[Hamid],
Residual Networks for Pulmonary Nodule Segmentation and Texture
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ICIAR20(II:396-405).
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2007
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Atwal, G.[Gurraj],
Phoulady, H.A.[Hady Ahmady],
Automatic Lung Cancer Follow-up Recommendation with 3d Deep Learning,
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2007
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Rassadin, A.[Alexandr],
Deep Residual 3d U-net for Joint Segmentation and Texture
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ICIAR20(II:419-427).
Springer DOI
2007
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Han, C.,
Kitamura, Y.,
Kudo, A.,
Ichinose, A.,
Rundo, L.,
Furukawa, Y.,
Umemoto, K.,
Li, Y.,
Nakayama, H.,
Synthesizing Diverse Lung Nodules Wherever Massively: 3D
Multi-Conditional GAN-Based CT Image Augmentation for Object
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3DV19(729-737)
IEEE DOI
1911
Training, Lung, Computed tomography,
Biomedical imaging, Generative adversarial networks,
3D Lung Nodule Detection
BibRef
Shaffie, A.,
Soliman, A.,
Khalifeh, H.A.,
Taher, F.,
Ghazal, M.,
Dunlap, N.,
Elmaghraby, A.,
Keynton, R.,
El-Baz, A.,
A Novel CT-Based Descriptors for Precise Diagnosis of Pulmonary
Nodules,
ICIP19(1400-1404)
IEEE DOI
1910
Computer Aided Diagnosis, RALBP, MGRF, Computer Tomography,
Spherical Harmonics, Autoencoder
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Zhang, G.,
Luo, Y.,
Zhu, D.,
Xu, Y.,
Sun, Y.,
Lu, J.,
Spatial Pyramid Dilated Network for Pulmonary Nodule Malignancy
Classification,
ICPR18(3911-3916)
IEEE DOI
1812
Convolution, Feature extraction,
Cancer, Computed tomography, Neural networks, Kernel
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Veduruparthi, B.K.,
Mukhopadhyay, J.,
Das, P.P.,
Saha, M.,
Prasath, S.,
Ray, S.,
Shrimali, R.K.,
Chatterjee, S.,
Segmentation of Lung Tumor in Cone Beam CT Images Based on Level-Sets,
ICIP18(1398-1402)
IEEE DOI
1809
Tumors, Image segmentation, Computed tomography,
Image edge detection, Light rail systems,
Local Rank Transform
BibRef
Zhu, W.,
Liu, C.,
Fan, W.,
Xie, X.,
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule
Detection and Classification,
WACV18(673-681)
IEEE DOI
1806
cancer, computerised tomography, feature extraction,
feedforward neural nets, image classification, image coding,
BibRef
Snezhko, E.V.,
Kharuzhyk, S.A.,
Tuzikov, A.V.,
Kovalev, V.A.,
Small Nodules Localization on CT Images of Lungs,
PTVSBB17(141-144).
DOI Link
1805
BibRef
Zhu, Y.,
Xu, Y.,
Ni, B.,
Zhang, J.,
Yang, X.,
Enhancing pulmonary nodule detection via cross-modal alignment,
VCIP17(1-4)
IEEE DOI
1804
computerised tomography, feature extraction,
image classification, lung, medical image processing,
Training
BibRef
Shaffie, A.,
Soliman, A.,
Ghazal, M.,
Taher, F.,
Dunlap, N.,
Wang, B.,
Elmaghraby, A.,
Gimel'farb, G.,
El-Baz, A.,
A new framework for incorporating appearance and shape features of
lung nodules for precise diagnosis of lung cancer,
ICIP17(1372-1376)
IEEE DOI
1803
Markov processes, cancer, computerised tomography,
feature extraction, image classification, image coding, Spherical Harmonics
BibRef
Xie, Y.T.[Yu-Tong],
Zhang, J.P.[Jian-Peng],
Liu, S.[Sidong],
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Xia, Y.[Yong],
Lung Nodule Classification by Jointly Using Visual Descriptors and Deep
Features,
MCV16(116-125).
Springer DOI
1711
BibRef
Pawelczyk, K.[Krzysztof],
Kawulok, M.[Michal],
Nalepa, J.[Jakub],
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McQuaid, S.J.[Sarah J.],
Prakash, V.[Vineet],
Ganeshan, B.[Balaji],
Towards Detecting High-Uptake Lesions from Lung CT Scans Using Deep
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CIAP17(II:310-320).
Springer DOI
1711
BibRef
Papiez, B.W.[Bartlomiej W.],
Brady, S.M.[Sir Michael],
Schnabel, J.A.[Julia A.],
Mass Transportation for Deformable Image Registration with Application
to Lung CT,
RAMBO17(66-74).
Springer DOI
1711
BibRef
Luo, Z.,
Brubaker, M.A.[Marcus A.],
Brudno, M.,
Size and Texture-Based Classification of Lung Tumors with 3D CNNs,
WACV17(806-814)
IEEE DOI
1609
Biological neural networks, Cancer, Computed tomography, Lungs,
Tumors
BibRef
Yan, X.J.[Xing-Jian],
Pang, J.N.[Jia-Ning],
Qi, H.[Hang],
Zhu, Y.X.[Yi-Xin],
Bai, C.X.[Chun-Xue],
Geng, X.[Xin],
Liu, M.[Mina],
Terzopoulos, D.[Demetri],
Ding, X.W.[Xiao-Wei],
Classification of Lung Nodule Malignancy Risk on Computed Tomography
Images Using Convolutional Neural Network: A Comparison Between 2D and
3D Strategies,
MCBMIIA16(III: 91-101).
Springer DOI
1704
BibRef
Bobadilla, J.C.M.[Julio Cesar Mendoza],
Pedrini, H.[Helio],
Lung Nodule Classification Based on Deep Convolutional Neural Networks,
CIARP16(117-124).
Springer DOI
1703
BibRef
Ishihara, M.[Masaki],
Matsuda, Y.J.[Yu-Ji],
Sugimura, M.[Masahiko],
Endo, S.[Susumu],
Takebe, H.[Hiroaki],
Baba, T.[Takayuki],
Uehara, Y.[Yusuke],
An Image Registration Method with Radial Feature Points Sampling:
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PSIVT15(512-525).
Springer DOI
1602
BibRef
Novo, J.[Jorge],
Goncalves, L.[Luis],
Mendonca, A.M.[Ana Maria],
Campilho, A.[Aurelio],
3D lung nodule candidate detection in multiple scales,
MVA15(61-64)
IEEE DOI
1507
Cancer
BibRef
Kumar, D.[Devinder],
Wong, A.[Alexander],
Clausi, D.A.[David A.],
Lung Nodule Classification Using Deep Features in CT Images,
CRV15(133-138)
IEEE DOI
1507
Accuracy
BibRef
Duggan, N.[Nóirín],
Bae, E.[Egil],
Shen, S.[Shiwen],
Hsu, W.[William],
Bui, A.[Alex],
Jones, E.[Edward],
Glavin, M.[Martin],
Vese, L.[Luminita],
A Technique for Lung Nodule Candidate Detection in CT Using Global
Minimization Methods,
EMMCVPR15(478-491).
Springer DOI
1504
BibRef
Liu, Y.[Yang],
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Li, P.[Ping],
Hidden conditional random field for lung nodule detection,
ICIP14(3518-3521)
IEEE DOI
1502
Biomedical imaging
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Forsberg, D.[Daniel],
Monsef, N.[Nastaran],
Evaluating Cell Nuclei Segmentation for Use on Whole-Slide Images in
Lung Cytology,
ICPR14(3380-3385)
IEEE DOI
1412
BibRef
Kaya, A.[Aydln],
Can, A.B.[Ahmet Burak],
eFis: A Fuzzy Inference Method for Predicting Malignancy of Small
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ICIAR14(II: 255-262).
Springer DOI
1410
BibRef
Gonçalves, L.[Luis],
Novo, J.[Jorge],
Campilho, A.[Aurélio],
Central Medialness Adaptive Strategy for 3D Lung Nodule Segmentation in
Thoracic CT Images,
ICIAR16(583-590).
Springer DOI
1608
BibRef
Novo, J.,
Rouco, J.,
Mendonça, A.,
Campilho, A.[Aurélio],
Reliable Lung Segmentation Methodology by Including Juxtapleural
Nodules,
ICIAR14(II: 227-235).
Springer DOI
1410
BibRef
Papiez, B.W.[Bartlomiej W.],
Tapmeier, T.[Thomas],
Heinrich, M.P.[Mattias P.],
Muschel, R.J.[Ruth J.],
Schnabel, J.A.[Julia A.],
Motion Correction of Intravital Microscopy of Preclinical Lung Tumour
Imaging Using Multichannel Structural Image Descriptor,
WBIR14(164-173).
Springer DOI
1407
BibRef
Lam, M.,
Doppa, J.R.,
Hu, X.[Xu],
Todorovic, S.,
Dietterich, T.,
Reft, A.,
Daly, M.,
Learning to Detect Basal Tubules of Nematocysts in SEM Images,
AccBio13(190-196)
IEEE DOI
1403
biology computing
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Li, Y.[Yang],
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Mixed Kernel Function SVM for Pulmonary Nodule Recognition,
CIAP13(II:449-458).
Springer DOI
1309
BibRef
Aggarwal, P.[Preeti],
Sardana, H.K.,
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Correlation between Biopsy Confirmed Cases and Radiologist's
Annotations in the Detection of Lung Nodules by Expanding the
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CAIP13(531-538).
Springer DOI
1308
BibRef
Vinay, K.,
Rao, A.,
Kumar, G.H.,
Computerized Analysis of Classification of Lung Nodules and Comparison
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IEEE DOI
1205
BibRef
Acharya, M.[Mekhala],
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An image analysis method for quantification of idiopathic pulmonary
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AIPR11(1-5).
IEEE DOI
1204
BibRef
Farag, A.A.[Amal A.],
Abd el Munim, H.E.[Hossam E.],
Graham, J.[James],
Farag, A.A.[Aly A.],
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El-Mogy, S.[Sabry],
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Variational approach for segmentation of lung nodules,
ICIP11(2157-2160).
IEEE DOI
1201
BibRef
Zinoveva, O.[Olga],
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A Texture-Based Probabilistic Approach for Lung Nodule Segmentation,
ICIAR11(II: 21-30).
Springer DOI
1106
BibRef
Farag, A.[Amal],
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Feature-Based Lung Nodule Classification,
ISVC10(III: 79-88).
Springer DOI
1011
BibRef
Choi, W.J.[Wook-Jin],
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Computer-aided detection of pulmonary nodules using genetic programming,
ICIP10(4353-4356).
IEEE DOI
1009
BibRef
Farag, A.A.[Amal A.],
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ICIP10(4281-4284).
IEEE DOI
1009
BibRef
Farag, A.A.[Amal A.],
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ICPR10(2588-2591).
IEEE DOI
1008
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Farag, A.A.[Amal A.],
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Lung Nodule Modeling: A Data-Driven Approach,
ISVC09(I: 347-356).
Springer DOI
0911
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Tolouee, A.,
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Texture Analysis in Lung HRCT Images,
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IEEE DOI
0812
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Shi, Z.H.[Zheng-Hao],
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A Method for Enhancing Lung Nodules in Chest Radiographs by Use of LoG
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CISP09(1-4).
IEEE DOI
0910
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Zheng, Y.J.[Yuan-Jie],
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MMBIA09(101-108).
IEEE DOI
0906
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Wei, E.[Erling],
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IEEE DOI
0812
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Suzuki, K.[Kenji],
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IEEE DOI
0812
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Bauer, C.[Christian],
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A Novel Approach for Detection of Tubular Objects and Its Application
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DAGM08(xx-yy).
Springer DOI
0806
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Zheng, Y.J.[Yuan-Jie],
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Lung Nodule Growth Analysis from 3D CT Data with a Coupled Segmentation
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MMBIA07(1-8).
IEEE DOI
0710
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Lung Nodule Detection using Eye-Tracking,
ICIP07(II: 457-460).
IEEE DOI
0709
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Memarian, N.,
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Computerized Detection of Lung Nodules with an Enhanced False Positive
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ICIP06(1921-1924).
IEEE DOI
0610
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Chromatographic Pattern Recognition Using Optimized One-Class
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IbPRIA09(449-456).
Springer DOI
0906
BibRef
Earlier:
Minimizing the Imbalance Problem in Chromatographic Profile
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ICIAR08(xx-yy).
Springer DOI
0806
BibRef
Earlier:
The Class Imbalance Problem in TLC Image Classification,
ICIAR06(II: 513-523).
Springer DOI
0610
BibRef
Pereira, C.S.[Carlos S.],
Fernandes, H.[Hugo],
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Detection of Lung Nodule Candidates in Chest Radiographs,
IbPRIA07(II: 170-177).
Springer DOI
0706
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Evaluation of Contrast Enhancement Filters for Lung Nodule Detection,
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Springer DOI
0708
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Pereira, C.S.[Carlos S.],
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ICIAR06(II: 612-623).
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0610
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Kubota, T.[Toshiro],
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Estimating Diameters of Pulmonary Nodules with Competition-Diffusion
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0601
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Campadelli, P.,
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Lung Nodules Detection and Classification,
ICIP05(I: 1117-1120).
IEEE DOI
0512
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Corrêa Silva, A.[Aristófanes],
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Semivariogram and SGLDM Methods Comparison for the Diagnosis of
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IbPRIA05(II:479).
Springer DOI
0509
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Silva, J.S.[José Silvestre],
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A Level-Set Based Volumetric CT Segmentation Technique:
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ICIAR04(II: 68-75).
Springer DOI
0409
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Nakamura, Y.,
Fukano, G.,
Takizawa, H.,
Mizuno, S.,
Yamamoto, S.,
Matsumoto, T.,
Tateno, Y.,
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Eigen nodule: view-based recognition of lung nodule in chest X-ray CT
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ICPR04(IV: 681-684).
IEEE DOI
0409
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Takizawa, H.,
Yamamoto, S.,
Matsumoto, T.,
Tateno, Y.,
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Matsumoto, M.,
Recognition of lung nodules from x-ray ct images using 3d markov random
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ICPR02(I: 99-102).
IEEE DOI
0211
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Mousa, W.A.H.,
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Lung nodule classification utilizing support vector machines,
ICIP02(III: 153-156).
IEEE DOI
0210
BibRef
Kawata, Y.,
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Kusumato, M.,
Kakinuma, R.,
Mori, K.,
Nishiyama, H.,
Eguchi, K.,
Kaneko, M.,
Moriyama, N.,
Three-dimensional CT image retrieval in a database of pulmonary nodules,
ICIP02(III: 149-152).
IEEE DOI
0210
BibRef
Kawata, Y.,
Niki, N.,
Ohmatsu, H.,
Kakinuma, R.,
Mori, K.,
Eguchi, K.,
Kaneko, M.,
Moriyama, N.,
Curvature based analysis of internal structure of pulmonary nodules
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ICIP98(III: 851-855).
IEEE DOI
9810
BibRef
Minami, K.,
Kawata, Y.,
Niki, N.,
Mori, K.,
Ohmatsu, H.,
Kakinuma, R.,
Eguchi, K.,
Kusumoto, M.,
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Moriyama, N.,
Computerized Characterization of Contrast Enhancement Patterns for
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ICIP01(II: 897-900).
IEEE DOI
0108
BibRef
Earlier:
Takagi, N., A2, A3 only:
ICIP00(Vol I: 188-191).
IEEE DOI
0008
BibRef
Kawata, Y.,
Niki, N.,
Ohmatsu, H.,
Kusumoto, M.,
Kakinuma, R.,
Mori, K.,
Nishiyama, H.,
Eguchi, K.,
Kaneko, M.,
Moriyama, N.,
Computerized Analysis of 3-d Pulmonary Nodule Images in Surrounding and
Internal Structure Feature Spaces,
ICIP01(II: 889-892).
IEEE DOI
0108
BibRef
Kubo, M.,
Kawata, Y.,
Niki, N.,
Eguchi, K.,
Ohmatsu, H.,
Kakinuma, R.,
Kaneko, M.,
Kusumoto, M.,
Moriyama, N.,
Mori, K.,
Nishiyama, H.,
Automatic Extraction of Pulmonary Fissures from Multidetector-row CT
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ICIP01(III: 1091-1094).
IEEE DOI
0108
BibRef
Kubota, K.,
Kubo, M.,
Kawata, Y.,
Niki, N.,
Eguchi, K.,
Omatsu, H.,
Kakinuma, R.,
Kaneko, M.,
Moriyama, N.,
The Results in the Clinical Trial of CAD System for Lung Cancer Using
Helical CT Images,
ICIP01(I: 313-316).
IEEE DOI
0108
BibRef
Kawata, Y.,
Niki, N.,
Surrounding Structures Analysis of Pulmonary Nodules Using Differential
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ICIP00(Vol III: 424-427).
IEEE DOI
0008
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And:
Internal Structure Analysis of Pulmonary Nodules in Topological and
Histogram Feature Spaces,
ICIP00(Vol I: 168-171).
IEEE DOI
0008
BibRef
Sammouda, M.,
Niki, N.,
Analysis of Color Images of Tissues Derived from Patients with
Adenocarcinoma of the Lung,
ICIP00(Vol I: 192-195).
IEEE DOI
0008
BibRef
Kubo, M.,
Niki, N.,
Eguchi, K.,
Kaneko, M.,
Kusumoto, M.,
Moriyama, N.,
Omatsu, H.,
Kakinuma, R.,
Nishiyama, H.,
Mori, K.,
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IEEE DOI
0009
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Yamamoto, T.,
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IEEE DOI
0008
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Kubo, M.,
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ICIP00(Vol II: 637-640).
IEEE DOI
0008
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Kubo, M.,
Tozaki, T.,
Niki, N.,
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Kaneko, M.,
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Bias Field Correction of Chest Thin Section CT Images,
ICIP97(III: 551-554).
IEEE DOI
BibRef
9700
Kawata, Y.,
Niki, N.,
Ohmatsu, H.,
Kusumoto, M.,
Kakinuma, R.,
Mori, K.,
Nishiyama, H.,
Eguchi, K.,
Kaneko, M.,
Moriyama, N.,
Computerized Analysis of Pulmonary Nodules in Topological and Histogram
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ICPR00(Vol IV: 332-335).
IEEE DOI
0009
BibRef
Kawata, Y.,
Niki, N.,
Ohmatsu, H.,
Kusumoto, M.,
Kakinuma, R.,
Mori, K.,
Nishiyama, H.,
Eguchi, K.,
Kaneko, M.,
Moriyama, N.,
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CIAP99(470-475).
IEEE DOI
9909
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Ohmatsu, H.[Hironobu],
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IEEE DOI
9808
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Kawata, Y.,
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Moriyama, N.,
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CIAP97(II: 420-427).
Springer DOI
9709
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Earlier:
Only: A2, A4, Add:
Kubo, M., A5, A6, A7, A8:
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IEEE DOI
9608
(Univ. of Tokushima, J)
BibRef
Tozaki, T.,
Kawata, Y.,
Niki, N.,
Ohmatsu, H.,
Eguchi, K.,
Moriyama, N.,
Three Dimensional Analysis of Lung Areas Using Thin Slice CT Images,
ICPR96(III: 548-552).
IEEE DOI
9608
(Univ. of Tokushima, J)
BibRef
Kawata, Y.,
Niki, N.,
Ohmatsu, H.,
Kakinuma, R.,
Kushmoto, M.,
Mori, K.,
Nishiyama, N.,
Eguchi, K.,
Kaneko, M.,
Moriyama, N.,
Classification of Pulmonary Nodules in Thin-section CT Images by Using
Multi-scale Curvature Indexes,
ICIP99(II:197-201).
IEEE DOI
BibRef
9900
Kawata, Y.,
Niki, N.,
Ohmatsu, H.,
Eguchi, K.,
Kaneko, M., and
Moriyama, N.,
Classification of Pulmonary Nodules in Thin Section CT Images Based
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ICIP97(III: 528-530).
IEEE DOI
BibRef
9700
Kawata, Y.[Yoshiki],
Kaneko, M.,
Eguchi, K.[Kenji],
Kakinuma, R.,
Moriyama, N.[Noriyuki],
Niki, N.[Noboru],
Ohmatsu, H.[Hironobu],
Curvature Based Analysis of Pulmonary Nodules Using
Thin-Section CT Images,
ICPR98(Vol I: 361-363).
IEEE DOI
9808
BibRef
Takagi, N.,
Kawata, Y.,
Niki, N.,
Mori, K.,
Ohmatsu, H.,
Kakinuma, R.,
Eguchi, K.,
Kusumoto, M.,
Kaneko, M.,
Moriyama, N.,
3D analysis of solitary pulmonary nodules based on contrast enhanced
dynamic CT,
ICIP99(III:416-420).
IEEE DOI
BibRef
9900
Tanaka, A.,
Tozaki, T.,
Kawata, Y.,
Niki, N.,
Ohmatsu, H.,
Kakimura, R.,
Kaneko, M.,
Eguchi, K.,
Moriyama, N.,
Pulmonary Organs Analysis Method and Its Evaluation Based on Thoracic
Thin-section CT Images,
ICIP99(III:421-425).
IEEE DOI
BibRef
9900
Mukaibo, T.,
Kawata, Y.,
Niki, N.,
Ohmatsu, H.,
Kakinuma, R.,
Kaneko, M.,
Eguchi, K.,
Moriyama, K.,
Classification of Pulmonary Blood Vessel Using Multidetector-row CT
Images,
ICIP01(II: 841-844).
IEEE DOI
0108
BibRef
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Tuberculosis Analysis, Tuberculosis Bacilli .