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breast cancer detection; computed tomography; magnetic resonance
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Digital mammography; CADx; Feature selection; SVM-RFE; Mutual
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digital tomosynthesis; iterative methods; beam hardening artifacts;
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1208
BibRef
Earlier:
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Springer DOI
0607
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van Schie, G.,
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1312
Breast Cancer
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Top, C.B.,
Gencer, N.G.,
Harmonic Motion Microwave Doppler Imaging:
A Simulation Study Using a Simple Breast Model,
MedImg(33), No. 2, February 2014, pp. 290-300.
IEEE DOI
1403
Doppler measurement
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Lakshmanan, M.N.,
Harrawood, B.P.,
Agasthya, G.A.,
Kapadia, A.J.,
Simulations of Breast Cancer Imaging Using Gamma-Ray Stimulated
Emission Computed Tomography,
MedImg(33), No. 2, February 2014, pp. 546-555.
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1403
Monte Carlo methods
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Han, L.[Lianghao],
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Barratt, D.,
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Hawkes, D.J.,
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1404
biomechanics
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Botterill, T.,
Lotz, T.,
Kashif, A.,
Chase, J.G.,
Reconstructing 3-D Skin Surface Motion for the DIET Breast Cancer
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1405
Breast cancer
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Boucher, A.[Arnaud],
Registration and analysis for images couple: Application to mammograms,
ELCVIA(13), No. 2, 2014, pp. xx-yy.
DOI Link
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Ph.D.. Thesis.
BibRef
Zhang, X.L.[Xiao-Lin],
Wang, W.[Wei],
Sze, G.,
Barber, D.,
Chatwin, C.,
An Image Reconstruction Algorithm for 3-D Electrical Impedance
Mammography,
MedImg(33), No. 12, December 2014, pp. 2223-2241.
IEEE DOI
1412
biological tissues
BibRef
Muralidhar, G.S.,
Bovik, A.C.,
Markey, M.K.,
Disparity Estimation on Stereo Mammograms,
IP(24), No. 9, September 2015, pp. 2851-2863.
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1506
Computational modeling
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Bevacqua, M.T.,
Scapaticci, R.,
A Compressive Sensing Approach for 3D Breast Cancer Microwave Imaging
With Magnetic Nanoparticles as Contrast Agent,
MedImg(35), No. 2, February 2016, pp. 665-673.
IEEE DOI
1602
Breast
BibRef
Park, S.,
Zhang, G.,
Myers, K.J.,
Comparison of Channel Methods and Observer Models for the Task-Based
Assessment of Multi-Projection Imaging in the Presence of Structured
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1606
Breast
BibRef
Jiang, Z.Q.[Zheng-Qiang],
Das, M.[Mini],
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Analyzing visual-search observers using eye-tracking data for digital
breast tomosynthesis images,
JOSA-A(34), No. 6, June 2017, pp. 838-845.
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1706
Image analysis, X-ray imaging, Vision-eye, movements
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Carneiro, G.,
Nascimento, J.,
Bradley, A.P.,
Automated Analysis of Unregistered Multi-View Mammograms With Deep
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MedImg(36), No. 11, November 2017, pp. 2355-2365.
IEEE DOI
1711
Breast, Cancer, Lesions, Machine learning, Mammography, Training,
Deep learning, mammogram, multi-view classification, transfer, learning
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Akhtar, Y.[Yusuf],
Mukherjee, D.P.[Dipti Prasad],
Reconstruction of three-dimensional linear structures in the breast
from craniocaudal and mediolateral oblique mammographic views,
IET-IPR(11), No. 11, November 2017, pp. 1114-1121.
DOI Link
1711
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Akhtar, Y.[Yusuf],
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Borges, L.R.,
Guerrero, I.,
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Vieira, M.A.C.,
Method for Simulating Dose Reduction in Digital Breast Tomosynthesis,
MedImg(36), No. 11, November 2017, pp. 2331-2342.
IEEE DOI
1711
Biomedical imaging, Breast, Calibration, Estimation,
Image reconstruction, Signal to noise ratio, Standards,
Electronic noise, digital breast tomosynthesis, dose reduction,
quantum, noise
BibRef
Makeev, A.,
Glick, S.J.,
Low-Dose Contrast-Enhanced Breast CT Using Spectral Shaping Filters:
An Experimental Study,
MedImg(36), No. 12, December 2017, pp. 2417-2423.
IEEE DOI
1712
Breast, Computed tomography, Image reconstruction, Iodine, Observers,
Phantoms, X-ray imaging, Contrast-enhanced Breast CT,
X-ray filter
BibRef
Zheng, J.,
Fessler, J.A.,
Chan, H.P.,
Detector Blur and Correlated Noise Modeling for Digital Breast
Tomosynthesis Reconstruction,
MedImg(37), No. 1, January 2018, pp. 116-127.
IEEE DOI
1801
biological tissues, computerised tomography, covariance matrices,
image reconstruction, iterative methods,
model-based iterative reconstruction
BibRef
Wang, H.Y.[Hong-Yu],
Feng, J.[Jun],
Zhang, Z.Z.[Zi-Zhao],
Su, H.[Hai],
Cui, L.[Lei],
He, H.[Hua],
Liu, L.[Li],
Breast mass classification via deeply integrating the contextual
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PR(80), 2018, pp. 42-52.
Elsevier DOI
1805
Mass classification, Multi-view, Mammography,
Convolutional Neural Networks, Recurrent Neural Networks
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Baran, P.,
Mayo, S.,
McCormack, M.,
Pacilč, S.,
Tromba, G.,
Dullin, C.,
Zanconati, F.,
Arfelli, F.,
Dreossi, D.,
Fox, J.,
Prodanovic, Z.,
Cholewa, M.,
Quiney, H.,
Dimmock, M.,
Nesterets, Y.,
Thompson, D.,
Brennan, P.,
Gureyev, T.,
High-Resolution X-Ray Phase-Contrast 3-D Imaging of Breast Tissue
Specimens as a Possible Adjunct to Histopathology,
MedImg(37), No. 12, December 2018, pp. 2642-2650.
IEEE DOI
1812
X-ray imaging, Australia, Breast tissue,
Breast cancer, Computed tomography, Computed tomography,
histology
BibRef
Samala, R.K.,
Chan, H.,
Hadjiiski, L.,
Helvie, M.A.,
Richter, C.D.,
Cha, K.H.,
Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of
Training Sample Size on Multi-Stage Transfer Learning Using Deep
Neural Nets,
MedImg(38), No. 3, March 2019, pp. 686-696.
IEEE DOI
1903
Breast, Task analysis, Training, Biomedical imaging, Cancer,
Training data, Breast cancer, computer-aided diagnosis,
transfer learning
BibRef
Rana, S.,
Hampson, R.,
Dobie, G.,
Breast Cancer: Model Reconstruction and Image Registration From
Segmented Deformed Image Using Visual and Force Based Analysis,
MedImg(39), No. 5, May 2020, pp. 1295-1305.
IEEE DOI
2005
Strain, Image reconstruction, Cameras, Breast,
Surface reconstruction, Force,
force deformation
BibRef
Gao, M.J.[Ming-Jie],
Fessler, J.A.[Jeffrey A.],
Chan, H.P.[Heang-Ping],
Deep Convolutional Neural Network With Adversarial Training for
Denoising Digital Breast Tomosynthesis Images,
MedImg(40), No. 7, July 2021, pp. 1805-1816.
IEEE DOI
2107
Training, Noise measurement, Noise reduction, Breast,
Image reconstruction, Phantoms, X-ray imaging,
microcalcification
BibRef
Jiang, G.F.[Gong-Fa],
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Chen, W.G.[Wei-Guo],
Lu, Y.[Yao],
Synthesis of Mammogram From Digital Breast Tomosynthesis Using Deep
Convolutional Neural Network With Gradient Guided cGANs,
MedImg(40), No. 8, August 2021, pp. 2080-2091.
IEEE DOI
2108
Image edge detection, generative adversarial networks,
Biomedical imaging, Linear programming, Mammography
BibRef
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Wang, S.[Shuai],
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Zhou, T.[Tao],
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Three-dimensional affinity learning based multi-branch ensemble
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PR(129), 2022, pp. 108723.
Elsevier DOI
2206
Breast tumor segmentation,
Three-dimensional affinity learning based refinement,
Multi-branch ensemble network
BibRef
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Liu, H.Q.[Hua-Qing],
Yang, T.[Tao],
Huang, M.Q.[Ming-Quan],
Zheng, B.W.[Bo-Wen],
Wu, T.[Tao],
Han, L.Q.[Lan-Qing],
Zhang, Y.[Yong],
Ren, J.[Jie],
Machine learning based on automated breast volume scanner (ABVS)
radiomics for differential diagnosis of benign and malignant BI-RADS
4 lesions,
IJIST(32), No. 5, 2022, pp. 1577-1587.
DOI Link
2209
automated breast volume scanner, breast neoplasms,
machine learning, radiomics
BibRef
Li, Z.J.[Zhi-Jin],
Carton, A.K.[Ann-Katherine],
Muller, S.[Serge],
Almecija, T.[Thomas],
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Desolneux, A.[Agnčs],
A 3D Mathematical Breast Texture Model With Parameters Automatically
Inferred From Clinical Breast CT Images,
MedImg(42), No. 4, April 2023, pp. 1107-1120.
IEEE DOI
2304
Breast, Mathematical models, Computed tomography, Ellipsoids,
Solid modeling, Phantoms, statistical parameter inference
BibRef
Deng, B.[Bin],
Gu, H.[Hanxue],
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Kalpathy-Cramer, J.[Jayashree],
Carp, S.A.[Stefan A.],
FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image
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MedImg(42), No. 8, August 2023, pp. 2439-2450.
IEEE DOI
2308
Image reconstruction, Optical imaging, Phantoms, Mathematical models, Breast,
breast cancer
BibRef
Ren, Y.[Yinhao],
Liu, X.[Xuan],
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Liang, Z.S.[Zi-Sheng],
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Go, J.[Jonathan],
Marks, J.R.[Jeffrey R.],
Lo, J.Y.[Joseph Y.],
Ipsilateral Lesion Detection Refinement for Tomosynthesis,
MedImg(42), No. 10, October 2023, pp. 3080-3090.
IEEE DOI
2310
BibRef
Komolafe, T.E.[Temitope Emmanuel],
Tian, Y.[Yuchi],
Awoniya, O.J.[Olanrewaju James],
Chen, S.Q.[Shuang-Qing],
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Comparative analysis of cone-beam breast computed tomography and
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IJIST(34), No. 3, 2024, pp. e23071.
DOI Link
2404
breast cancer, cone-beam breast computed tomography,
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Zhu, M.[Manman],
Wang, Z.[Zidan],
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Wang, Y.B.[Yong-Bo],
VBVT-Net: VOI-Based VVBP-Tensor Network for High-Attenuation Artifact
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MedImg(44), No. 4, April 2025, pp. 1953-1968.
IEEE DOI
2504
Imaging, Image reconstruction, Detectors, Biomedical imaging,
Trajectory, Tensors, Filtering, Reconstruction algorithms, Noise,
view-by-view backprojection tensor
BibRef
Xu, F.[Fei],
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Breast Anatomy Enriched Tumor Saliency Estimation,
ICPR21(2904-2911)
IEEE DOI
2105
Image segmentation, Ultrasonic imaging, Semantics, Neural networks,
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Liu, Y.,
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Wang, Y.,
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IEEE DOI
2008
Bipartite graph, Visualization, Cognition, Breast, Mammography,
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Wang, H.,
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Zwiggelaar, R.,
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ICIP18(2072-2076)
IEEE DOI
1809
Breast, Measurement, Cancer, Radio frequency, Shape, Reliability,
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Bessa, S.[Sílvia],
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Registration of Breast Surface Data Before and After Surgical
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IbPRIA17(226-234).
Springer DOI
1706
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Springer DOI
1706
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Zolfagharnasab, H.[Hooshiar],
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Springer DOI
1706
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Islam, M.T.,
Keynote speaker: Electromagnetic performances analysis of an
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IVPR17(1-2)
IEEE DOI
1704
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Allegra, D.[Dario],
Milotta, F.L.M.[Filippo L. M.],
Sinitň, D.[Diego],
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1711
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ICIP15(252-256)
IEEE DOI
1512
Digital breast tomosynthesis; ipsilateral view; region matching
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Kim, D.H.[Dae Hoe],
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ICIP15(4521-4524)
IEEE DOI
1512
Digital breast tomosynthesis
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Biomedical image processing
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CVRMed95(XX-YY)
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CVRMed95(XX-YY)
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9500
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