Levman, J.,
Leung, T.,
Causer, P.,
Plewes, D.,
Martel, A.L.,
Classification of Dynamic Contrast-Enhanced Magnetic Resonance Breast
Lesions by Support Vector Machines,
MedImg(27), No. 5, May 2008, pp. 688-696.
IEEE DOI
0711
BibRef
Gal, Y.[Yaniv],
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Bradley, A.P.[Andrew P.],
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Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal
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IEEE DOI
1002
BibRef
Earlier: A1, A2, A3, A5, A6, Only:
Feature and Classifier Selection for Automatic Classification of
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IEEE DOI
0912
BibRef
Nagarajan, M.B.[Mahesh B.],
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Schlossbauer, T.[Thomas],
Leinsinger, G.[Gerda],
Krol, A.[Andrzej],
Wismüller, A.[Axel],
Classification of small lesions in dynamic breast MRI: eliminating the
need for precise lesion segmentation through spatio-temporal analysis
of contrast enhancement,
MVA(24), No. 7, October 2013, pp. 1371-1381.
Springer DOI
1309
BibRef
Earlier:
Classifying Small Lesions on Breast MRI through Dynamic Enhancement
Pattern Characterization,
MLMI11(352-359).
Springer DOI
1109
BibRef
Soares, F.,
Janela, F.,
Pereira, M.,
Seabra, J.,
Freire, M.M.,
3D Lacunarity in Multifractal Analysis of Breast Tumor Lesions in
Dynamic Contrast-Enhanced Magnetic Resonance Imaging,
IP(22), No. 11, 2013, pp. 4422-4435.
IEEE DOI
1310
biological organs
BibRef
Platel, B.,
Mus, R.,
Welte, T.,
Karssemeijer, N.,
Mann, R.,
Automated Characterization of Breast Lesions Imaged With an Ultrafast
DCE-MR Protocol,
MedImg(33), No. 2, February 2014, pp. 225-232.
IEEE DOI
1403
biomedical MRI
BibRef
Ribes, S.,
Didierlaurent, D.,
Decoster, N.,
Gonneau, E.,
Risser, L.,
Feillel, V.,
Caselles, O.,
Automatic Segmentation of Breast MR Images Through a Markov Random
Field Statistical Model,
MedImg(33), No. 10, October 2014, pp. 1986-1996.
IEEE DOI
1411
Markov processes
BibRef
Khalvati, F.,
Gallego-Ortiz, C.,
Balasingham, S.,
Martel, A.L.,
Automated Segmentation of Breast in 3-D MR Images Using a Robust
Atlas,
MedImg(34), No. 1, January 2015, pp. 116-125.
IEEE DOI
1502
biological organs
BibRef
Rasti, R.[Reza],
Teshnehlab, M.[Mohammad],
Phung, S.L.[Son Lam],
Breast cancer diagnosis in DCE-MRI using mixture ensemble of
convolutional neural networks,
PR(72), No. 1, 2017, pp. 381-390.
Elsevier DOI
1708
Breast, cancer
BibRef
Garcia, E.,
Diez, Y.,
Diaz, O.,
Llado, X.,
Gubern-Merida, A.,
Marti, R.,
Marti, J.,
Oliver, A.,
Multimodal Breast Parenchymal Patterns Correlation Using a
Patient-Specific Biomechanical Model,
MedImg(37), No. 3, March 2018, pp. 712-723.
IEEE DOI
1804
biological organs, biological tissues, biomechanics,
biomedical MRI, elasticity, image registration,
subject-specific biomechanical models
BibRef
Kallel, I.K.[I. Khanfir],
Almouahed, S.,
Solaiman, B.,
Bosse, E.,
An iterative possibilistic knowledge diffusion approach for blind
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PR(78), 2018, pp. 182-197.
Elsevier DOI
1804
Possibilistic knowledge representation,
Knowledge diffusion modeling, Iterative segmentation,
Mammographic medical images
BibRef
Zhang, L.,
Jiang, S.,
Zhao, Y.,
Feng, J.,
Pogue, B.W.,
Paulsen, K.D.,
Direct Regularization From Co-Registered Contrast MRI Improves Image
Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast
Lesions,
MedImg(37), No. 5, May 2018, pp. 1247-1252.
IEEE DOI
1805
Breast, Cancer, Image reconstruction, Optical fibers, Tumors,
Optical imaging, breast, image reconstruction, magnetic resonance imaging
BibRef
Piantadosi, G.[Gabriele],
Marrone, S.[Stefano],
Fusco, R.[Roberta],
Sansone, M.[Mario],
Sansone, C.[Carlo],
Comprehensive computer-aided diagnosis for breast T1-weighted DCE-MRI
through quantitative dynamical features and spatio-temporal local
binary patterns,
IET-CV(12), No. 7, October 2018, pp. 1007-1017.
DOI Link
1809
BibRef
Gravina, M.[Michela],
Marrone, S.[Stefano],
Piantadosi, G.[Gabriele],
Sansone, M.[Mario],
Sansone, C.[Carlo],
3TP-CNN: Radiomics and Deep Learning for Lesions Classification in
DCE-MRI,
CIAP19(II:661-671).
Springer DOI
1909
BibRef
Zhang, J.,
Saha, A.,
Zhu, Z.,
Mazurowski, M.A.[Maciej A.],
Hierarchical Convolutional Neural Networks for Segmentation of Breast
Tumors in MRI With Application to Radiogenomics,
MedImg(38), No. 2, February 2019, pp. 435-447.
IEEE DOI
1902
Image segmentation, Breast tumors, Lesions, Feature extraction,
Breast cancer,
molecular subtype classification
BibRef
Zhu, Z.[Zhe],
Mittendorf, A.[Amber],
Shropshire, E.[Erin],
Allen, B.[Brian],
Miller, C.[Chad],
Bashir, M.R.[Mustafa R.],
Mazurowski, M.A.[Maciej A.],
3D Pyramid Pooling Network for Abdominal MRI Series Classification,
PAMI(44), No. 4, April 2022, pp. 1688-1698.
IEEE DOI
2203
Magnetic resonance imaging, Biomedical imaging, Liver,
Task analysis, Convolutional neural networks, Annotations,
3D pyramid pooling network
BibRef
Biswas, B.[Biswajit],
Ghosh, S.K.[Swarup Kr],
Ghosh, A.[Anupam],
A novel automated magnetic resonance image segmentation approach
based on elliptical gamma mixture model for breast lumps detection,
IJIST(29), No. 4, 2019, pp. 599-616.
DOI Link
1911
breast hamartoma,
computation unified device architecture (CUDA),
semisupervised classifier
BibRef
Whitney, H.M.[Heather M.],
Li, H.[Hui],
Ji, Y.[Yu],
Liu, P.F.[Pei-Fang],
Giger, M.L.[Maryellen L.],
Comparison of Breast MRI Tumor Classification Using Human-Engineered
Radiomics, Transfer Learning From Deep Convolutional Neural Networks,
and Fusion Methods,
PIEEE(108), No. 1, January 2020, pp. 163-177.
IEEE DOI
2001
Feature extraction, Lesions, Biomedical imaging, Breast cancer,
Cancer, Magnetic resonance imaging, Breast cancer,
transfer learning
BibRef
Wu, C.,
Hormuth, D.A.,
Oliver, T.A.,
Pineda, F.,
Lorenzo, G.,
Karczmar, G.S.,
Moser, R.D.,
Yankeelov, T.E.,
Patient-Specific Characterization of Breast Cancer Hemodynamics Using
Image-Guided Computational Fluid Dynamics,
MedImg(39), No. 9, September 2020, pp. 2760-2771.
IEEE DOI
2009
Magnetic resonance imaging, Tumors, Computational fluid dynamics,
Computational modeling, Cancer, Hemodynamics, Tumor, 1D-3D coupled,
diffusion MRI
BibRef
Holste, G.[Gregory],
Partridge, S.C.[Savannah C.],
Rahbar, H.[Habib],
Biswas, D.[Debosmita],
Lee, C.I.[Christoph I.],
Alessio, A.M.[Adam M.],
End-to-End Learning of Fused Image and Non-Image Features for
Improved Breast Cancer Classification from MRI,
CVAMD21(3287-3296)
IEEE DOI
2112
Deep learning, Sensitivity, Magnetic resonance imaging,
Receivers, Medical services, Predictive models
BibRef
Li, A.J.[Ai-Jing],
Pan, Y.N.[Yu-Ning],
Chen, B.[Bin],
Huang, R.[Rong],
Xia, J.[Jianbi],
Jin, Y.H.[Yin-Hua],
Zheng, J.J.[Jian-Jun],
Evaluation of quantitative dynamic contrast-enhanced (DCE)-MRI
parameters using a reference region model in invasive ductal
carcinoma (IDC) patients,
IJIST(31), No. 1, 2021, pp. 215-222.
DOI Link
2102
breast invasive ductal carcinoma, DCE-MRI, prognostic factors
BibRef
Shrivastava, N.[Neeraj],
Bharti, J.[Jyoti],
Breast Tumor Detection in MR Images Based on Density,
IJIG(22), No. 1 2022, pp. 2250001.
DOI Link
2202
BibRef
Feng, B.[Bao],
Zhou, H.Y.[Hao-Yang],
Feng, J.[Jin],
Chen, Y.H.[Ye-Hang],
Liu, Y.[Yu],
Yu, T.Y.[Tian-You],
Liu, Z.S.[Zhuang-Sheng],
Long, W.S.[Wan-Sheng],
Active contour model of breast cancer DCE-MRI segmentation with an
extreme learning machine and a fuzzy C-means cluster,
IET-IPR(16), No. 11, 2022, pp. 2947-2958.
DOI Link
2208
BibRef
Stelter, J.K.[Jonathan K.],
Boehm, C.[Christof],
Ruschke, S.[Stefan],
Weiss, K.[Kilian],
Diefenbach, M.N.[Maximilian N.],
Wu, M.M.[Ming-Ming],
Borde, T.[Tabea],
Schmidt, G.P.[Georg P.],
Makowski, M.R.[Marcus R.],
Fallenberg, E.M.[Eva M.],
Karampinos, D.C.[Dimitrios C.],
Hierarchical Multi-Resolution Graph-Cuts for Water-Fat-Silicone
Separation in Breast MRI,
MedImg(41), No. 11, November 2022, pp. 3253-3265.
IEEE DOI
2211
Fats, Chemicals, Estimation, Breast, In vivo, Spatial resolution,
Magnetic resonance imaging,
silicone implants
BibRef
Pereira, T.M.C.[Teresa M. C.],
Pelicano, A.C.[Ana Catarina],
Godinho, D.M.[Daniela M.],
Gonçalves, M.C.T.[Maria C. T.],
Castela, T.[Tiago],
Orvalho, M.L.[Maria Lurdes],
Sencadas, V.[Vitor],
Sebastião, R.[Raquel],
Conceição, R.C.[Raquel C.],
Breast MRI Multi-tumor Segmentation Using 3d Region Growing,
CIARP23(II:15-29).
Springer DOI
2312
BibRef
Khaled, R.[Roa'a],
Vidal, J.[Joel],
Martí, R.[Robert],
Deep Learning Based Segmentation of Breast Lesions in DCE-MRI,
AIHA20(417-430).
Springer DOI
2103
BibRef
Aprea, F.[Federica],
Marrone, S.[Stefano],
Sansone, C.[Carlo],
Neural Machine Registration for Motion Correction in Breast DCE-MRI,
ICPR21(4332-4339)
IEEE DOI
2105
Face recognition, Brightness, Dynamics, Focusing,
Artificial neural networks, Distortion, Physiology
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Galli, A.[Antonio],
Gravina, M.[Michela],
Marrone, S.[Stefano],
Piantadosi, G.[Gabriele],
Sansone, M.[Mario],
Sansone, C.[Carlo],
Evaluating Impacts of Motion Correction on Deep Learning Approaches for
Breast DCE-MRI Segmentation and Classification,
CAIP19(II:294-304).
Springer DOI
1909
BibRef
Soleimani, H.[Hossein],
Rincon, J.[Jose],
Michailovich, O.V.[Oleg V.],
Segmentation of Breast MRI Scans in the Presence of Bias Fields,
ICIAR19(I:376-387).
Springer DOI
1909
BibRef
Piantadosi, G.,
Sansone, M.,
Sansone, C.,
Breast Segmentation in MRI via U-Net Deep Convolutional Neural
Networks,
ICPR18(3917-3922)
IEEE DOI
1812
Image segmentation, Breast, Task analysis,
Proposals, Convolution
BibRef
Fabijanska, A.[Anna],
Vacavant, A.[Antoine],
Lebre, M.A.[Marie-Ange],
Pavan, A.L.M.[Ana L. M.],
de Pina, D.R.[Diana R.],
Abergel, A.[Armand],
Chabrot, P.[Pascal],
Magnin, B.[Benoît],
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based
on an U-Net Framework,
ICCVG18(319-328).
Springer DOI
1810
BibRef
Comelli, A.[Albert],
Bruno, A.[Alessandro],
di Vittorio, M.L.[Maria Laura],
Ienzi, F.[Federica],
Lagalla, R.[Roberto],
Vitabile, S.[Salvatore],
Ardizzone, E.[Edoardo],
Automatic Multi-seed Detection for MR Breast Image Segmentation,
CIAP17(I:706-717).
Springer DOI
1711
BibRef
Marrone, S.,
Piantadosi, G.,
Fusco, R.,
Petrillo, A.,
Sansone, M.,
Sansone, C.,
Breast segmentation using Fuzzy C-Means and anatomical priors in
DCE-MRI,
ICPR16(1472-1477)
IEEE DOI
1705
Breast, Heart, Image edge detection, Image segmentation, Lesions,
Muscles, Breast DCE-MRI, Fuzzy C-Means,
Segmentation
BibRef
Tzalavra, A.[Alexia],
Dalakleidi, K.[Kalliopi],
Zacharaki, E.I.[Evangelia I.],
Tsiaparass, N.[Nikolaos],
Constantinidis, F.[Fotios],
Paragios, N.[Nikos],
Nikita, K.S.[Konstantina S.],
Comparison of Multi-resolution Analysis Patterns for Texture
Classification of Breast Tumors Based on DCE-MRI,
MLMI16(296-304).
Springer DOI
1611
BibRef
Razavi, M.[Mohammad],
Wang, L.[Lei],
Tan, T.[Tao],
Karssemeijer, N.[Nico],
Linsen, L.[Lars],
Frese, U.[Udo],
Hahn, H.K.[Horst K.],
Zachmann, G.[Gabriel],
Novel Morphological Features for Non-mass-like Breast Lesion
Classification on DCE-MRI,
MLMI16(305-312).
Springer DOI
1611
BibRef
Urbán, S.[Szabolcs],
Ruskó, L.[László],
Nagy, A.[Antal],
A Self-learning Tumor Segmentation Method on DCE-MRI Images,
ICIAR16(591-598).
Springer DOI
1608
BibRef
Razavi, M.[Mohammad],
Wang, L.[Lei],
Gubern-Mérida, A.[Albert],
Ivanovska, T.[Tatyana],
Laue, H.[Hendrik],
Karssemeijer, N.[Nico],
Hahn, H.K.[Horst K.],
Towards Accurate Segmentation of Fibroglandular Tissue in Breast MRI
Using Fuzzy C-Means and Skin-Folds Removal,
CIAP15(I:528-536).
Springer DOI
1511
BibRef
Maken, F.A.,
Gal, Y.,
McClymont, D.,
Bradley, A.P.,
Multiple Instance Learning for Breast Cancer Magnetic Resonance
Imaging,
DICTA14(1-8)
IEEE DOI
1502
biomedical MRI
BibRef
Liu, Y.P.[Yi-Ping],
Liu, H.[Hui],
Zhao, Z.W.[Zuo-Wei],
Zhang, L.[Lina],
Liu, X.[Xiang],
A new active contour model-based segmentation approach for accurate
extraction of the lesion from breast DCE-MRI,
ICIP13(1140-1143)
IEEE DOI
1402
Active contours
BibRef
Srikantha, A.[Abhilash],
Symmetry-Based Detection and Diagnosis of DCIS in Breast MRI,
GCPR13(255-260).
Springer DOI
1311
BibRef
Marrone, S.[Stefano],
Piantadosi, G.[Gabriele],
Automatic Lesion Detection in Breast DCE-MRI,
CIAP13(II:359-368).
Springer DOI
1309
BibRef
Liang, X.[Xi],
Ramamohanara, K.,
Frazer, H.,
Yang, Q.[Qing],
Lesion Segmentation in Dynamic Contrast Enhanced MRI of Breast,
DICTA12(1-8).
IEEE DOI
1303
BibRef
Marrone, S.[Stefano],
Piantadosi, G.[Gabriele],
Fusco, R.[Roberta],
Petrillo, A.[Antonella],
Sansone, M.[Mario],
Sansone, C.[Carlo],
An Investigation of Deep Learning for Lesions Malignancy Classification
in Breast DCE-MRI,
CIAP17(II:479-489).
Springer DOI
1711
BibRef
Piantadosi, G.[Gabriele],
Fusco, R.[Roberta],
Petrillo, A.[Antonella],
Sansone, M.[Mario],
Sansone, C.[Carlo],
LBP-TOP for Volume Lesion Classification in Breast DCE-MRI,
CIAP15(I:647-657).
Springer DOI
1511
BibRef
Earlier: A2, A4, A3, A5, Only:
A Multiple Classifier System for Classification of Breast Lesions Using
Dynamic and Morphological Features in DCE-MRI,
SSSPR12(684-692).
Springer DOI
1211
BibRef
Gravina, M.[Michela],
Marrone, S.[Stefano],
Sansone, M.[Mario],
Sansone, C.[Carlo],
DAE-CNN: Exploiting and disentangling contrast agent effects for
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PRL(145), 2021, pp. 67-73.
Elsevier DOI
2104
BibRef
Fusco, R.[Roberta],
Sansone, M.[Mario],
Sansone, C.[Carlo],
Petrillo, A.[Antonella],
Selection of Suspicious ROIs in Breast DCE-MRI,
CIAP11(I: 48-57).
Springer DOI
1109
BibRef
Tao, Y.[Yimo],
Xuan, J.H.[Jian-Hua],
Freedman, M.T.[Matthew T.],
Chepko, G.[Gloria],
Shields, P.G.[Peter G.],
Wang, Y.[Yue],
Imaging biomarker analysis of rat mammary fat pads and glandular
tissues in MRI images,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Meyer-Baese, A.,
Lange, O.,
Schlossbauer, T.,
Wismuller, A.,
Computer-aided diagnosis and visualization based on clustering and
independent component analysis for breast MRI,
ICIP08(3000-3003).
IEEE DOI
0810
BibRef
d'Elia, C.,
Marrocco, C.,
Molinara, M.,
Poggi, G.,
Scarpa, G.,
Tortorella, F.,
Detection of microcalcifications clusters in mammograms through TS-MRF
segmentation and SVM-based classification,
ICPR04(III: 742-745).
IEEE DOI
0409
BibRef
Marrocco, C.[Claudio],
Molinara, M.[Mario],
Tortorella, F.[Francesco],
Exploring Cascade Classifiers for Detecting Clusters of
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CIAP11(I: 384-392).
Springer DOI
1109
BibRef
Marrocco, C.[Claudio],
Molinara, M.[Mario],
Tortorella, F.[Francesco],
Algorithms for Detecting Clusters of Microcalcifications in Mammograms,
CIAP05(884-891).
Springer DOI
0509
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Mammograms, Three Dimensional Analysis, Registration .