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Sampat, M.P.,
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Mass segmentation; Computer-aided diagnose; Vector-valued level set;
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Accuracy
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bioelectric potentials
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Monte Carlo methods
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cancer
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Background: Recurrence is an important cornerstone in breast cancer
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Breast, Cancer, Feature extraction, Gaze tracking, Lesions,
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1705
Arteries, Breast, Calcium, Diseases, Machine learning, Mammography,
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Accuracy
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Breast
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1707
Breast tissue, Ducts, Image color analysis, Image segmentation,
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Mammography
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High Energy Resolution Hyperspectral X-Ray Imaging for Low-Dose
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biological organs,
dense breasts, image registration,
motion artifacts, Lesions, spectroscopy
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1712
Binary codes, Breast cancer, Databases, Mammography,
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IEEE DOI
1808
Antenna measurements, Radar imaging, Breast, Microwave imaging,
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biological tissues, cancer, cellular biophysics, computer vision,
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IEEE DOI
1901
Breast, Dielectrics, Image reconstruction, Permittivity,
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1902
Tumors, Biological tissues, Breast cancer, Solid modeling,
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Breast cancer, Image segmentation, Metastasis, Tumors, Task analysis,
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Breast cancer
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Image classification, CNN, Quality score
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Breast cancer, Deep learning, Quality-related,
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1911
Predictive models, Task analysis, Tumors, Computational modeling,
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2002
Mixed-supervision, deep learning,
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Breast, Estrogen, Progesterone, Encoder, Decoder
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UWB-SAR, CSAR, microwave imaging, breast tumour
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2004
Breast cancer, Task analysis, Biomedical imaging,
Predictive models, Training, Deep learning,
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Lesions, Mammography, Image segmentation, Breast cancer,
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Data mining and machine learning, Breast cancer,
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breast cancer, convolutional neural networks,
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3D phantom, breast cancer, dielectric measurement, holography, tumor detection
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Invasive ductal carcinoma, Whole slide images, Deep learning, Transfer learning
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Deep Neural Networks With Region-Based Pooling Structures for
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2006
Mammographic image, breast cancer, deep neural networks
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2006
Histopathological image, reinforcement learning,
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breast cancer, convolutional neural network, data augmentation,
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breast cancer, computer-aided diagnosis, histopathology,
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breast cancer, collective animal behavior (CAB) algorithm,
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fully convolutional network, mammogram, mass segmentation,
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digital mammography, pectoral muscle segmentation
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Detectors, II-VI semiconductor materials, Cadmium compounds,
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Uncertainty, Image segmentation, Breast, Reliability, Training,
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Huang, L.,
Han, M.,
Zhang, Y.,
Ma, J.,
DeepLIMa: Deep Learning Based Lesion Identification in Mammograms,
VRMI19(362-370)
IEEE DOI
2004
Mammography, Breast, Lesions, Machine learning, Task analysis,
Neural networks, Mammography, Lesion detection,
Deep learning
BibRef
Lee, H.,
Kim, S.T.,
Ro, Y.M.,
Building a Breast-Sentence Dataset:
Its Usefulness for Computer-Aided Diagnosis,
VRMI19(440-449)
IEEE DOI
2004
cancer, learning (artificial intelligence), mammography,
medical image processing, natural language processing,
Visual pointing
BibRef
Saini, M.[Manisha],
Susan, S.[Seba],
Data Augmentation of Minority Class with Transfer Learning for
Classification of Imbalanced Breast Cancer Dataset Using Inception-V3,
IbPRIA19(I:409-420).
Springer DOI
1910
BibRef
Oliveira, H.S.[Hugo S.],
Teixeira, J.F.[João F.],
Oliveira, H.P.[Hélder P.],
Lightweight Deep Learning Pipeline for Detection, Segmentation and
Classification of Breast Cancer Anomalies,
CIAP19(II:707-715).
Springer DOI
1909
BibRef
Liu, X.F.[Xiao-Feng],
Zou, Y.[Yang],
Song, Y.H.[Yu-Hang],
Yang, C.[Chao],
You, J.[Jane],
Kumar, B.V.K.V.[B. V. K. Vijaya],
Ordinal Regression with Neuron Stick-Breaking for Medical Diagnosis,
BioIm18(VI:335-344).
Springer DOI
1905
BibRef
Domingues, I.,
Abreu, P.H.,
Santos, J.,
Bi-Rads Classification of Breast Cancer: A New Pre-Processing
Pipeline for Deep Models Training,
ICIP18(1378-1382)
IEEE DOI
1809
Training, Machine learning, Databases, Breast cancer,
Mammography, Image pre-processing, Deep learning,
BI-RADS classification
BibRef
Weiss, N.[Nick],
Kost, H.[Henning],
Homeyer, A.[André],
Towards Interactive Breast Tumor Classification Using Transfer Learning,
ICIAR18(727-736).
Springer DOI
1807
BibRef
Mehta, S.,
Mercan, E.,
Bartlett, J.,
Weaver, D.,
Elmore, J.,
Shapiro, L.,
Learning to Segment Breast Biopsy Whole Slide Images,
WACV18(663-672)
IEEE DOI
1806
biological tissues, decoding, feature extraction,
image classification, image resolution, image segmentation,
Semantics
BibRef
Lin, H.,
Chen, H.,
Dou, Q.,
Wang, L.,
Qin, J.,
Heng, P.A.,
ScanNet: A Fast and Dense Scanning Framework for Metastastic Breast
Cancer Detection from Whole-Slide Image,
WACV18(539-546)
IEEE DOI
1806
cancer, computerised tomography, feature extraction, gynaecology,
image classification, medical image processing, tumours,
Training
BibRef
Nahid, A.A.,
Kong, Y.,
Local and Global Feature Utilization for Breast Image Classification
by Convolutional Neural Network,
DICTA17(1-6)
IEEE DOI
1804
biological organs, convolution, feature extraction,
image classification, medical image processing, neural nets,
Kernel
BibRef
Dong, Y.,
Shen, X.J.,
Wang, L.J.,
Wornyo, D.,
Zha, Z.J.,
Diversity-induced weighted classifier ensemble learning,
ICIP17(1232-1236)
IEEE DOI
1803
Breast cancer, Diversity reception, Heart, Minimization, Sonar,
Stability criteria, Training, classifier diversity,
weighted classifier
BibRef
Yi, C.Q.[Cong-Qin],
Zhou, R.Y.[Ru-Yan],
Hu, K.N.[Ke-Ning],
Fuzzy Support Vector Machine for breast cancer gene classification,
ICIVC17(676-679)
IEEE DOI
1708
Programming, Support vector machines, FSVM, SVM, classification, gene
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Shrivastava, A.,
Chaudhary, A.,
Kulshreshtha, D.,
Singh, V.P.[V. Prakash],
Srivastava, R.,
Automated digital mammogram segmentation using Dispersed Region
Growing and Sliding Window Algorithm,
ICIVC17(366-370)
IEEE DOI
1708
Classification algorithms, Image analysis, Image segmentation,
Labeling, Mammography, CAD,
Dispersed Region Growing Algorithm (DRGA),
Sliding Window Algorithm (SWA), image segmentation, mammography
BibRef
Bayramoglu, N.,
Kannala, J.,
Heikkilä, J.,
Deep learning for magnification independent breast cancer
histopathology image classification,
ICPR16(2440-2445)
IEEE DOI
1705
Breast cancer, Databases, Microscopy, Pathology, Training, Training, data
BibRef
Alcântara, R.[Rafaela],
Junior, P.F.[Perfilino Ferreira],
Ramos, A.[Aline],
Tsallis Entropy Extraction for Mammographic Region Classification,
CIARP16(451-458).
Springer DOI
1703
BibRef
Dhahbi, S.[Sami],
Barhoumi, W.[Walid],
Zagrouba, E.[Ezzeddine],
Content-Based Mammogram Retrieval Using Mixed Kernel PCA and Curvelet
Transform,
ACIVS16(582-590).
Springer DOI
1611
BibRef
Král, P.,
Lenc, L.,
LBP features for breast cancer detection,
ICIP16(2643-2647)
IEEE DOI
1610
Breast cancer
BibRef
Verma, R.,
Kumar, N.,
Sethi, A.,
Gann, P.H.,
Detecting multiple sub-types of breast cancer in a single patient,
ICIP16(2648-2652)
IEEE DOI
1610
Breast cancer
BibRef
Goubalan, S.R.T.J.,
Goussard, Y.,
Maaref, H.,
Unsupervised malignant mammographic breast mass segmentation
algorithm based on pickard Markov random field,
ICIP16(2653-2657)
IEEE DOI
1610
Breast
BibRef
Fiallos, C.B.,
Pérez, M.G.,
Conci, A.,
Andaluz, V.H.,
Automatic detection of injuries in mammograms using image analysis
techniques,
WSSIP15(245-248)
IEEE DOI
1603
cancer
BibRef
Khan, N.[Nabeel],
Wang, K.[Kaier],
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Highnam, R.[Ralph],
Automatic BI-RADS Classification of Mammograms,
PSIVT15(475-487).
Springer DOI
1602
BibRef
Dhungel, N.[Neeraj],
Carneiro, G.[Gustavo],
Bradley, A.P.[Andrew P.],
Automated Mass Detection in Mammograms Using Cascaded Deep Learning
and Random Forests,
DICTA15(1-8)
IEEE DOI
1603
BibRef
And:
Deep structured learning for mass segmentation from mammograms,
ICIP15(2950-2954)
IEEE DOI
1512
belief networks.
Mammograms; mass segmentation; structured inference; structured learning
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Guo, M.[Miao],
Dong, M.[Mev],
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A new method for mammographic mass segmentation based on parametric
active contour model,
ICWAPR15(27-33)
IEEE DOI
1511
cancer
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Rodriguez, J.C.[Juan Cruz],
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Integrative Functional Analysis Improves Information Retrieval in
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CIARP15(43-50).
Springer DOI
1511
BibRef
Galdran, A.[Adrian],
Picón, A.[Artzai],
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Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture
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IbPRIA15(587-594).
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1506
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Improving Bayesian Networks Breast Mass Diagnosis by Using Clinical
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MCPR15(292-301).
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1506
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ICIP14(912-916)
IEEE DOI
1502
Breast
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Gharsalli, L.,
Duchene, B.,
Mohammad-Djafari, A.,
Ayasso, H.,
A gradient-like variational Bayesian approach: Application to
microwave imaging for breast tumor detection,
ICIP14(1708-1712)
IEEE DOI
1502
Approximation methods
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Ayasso, H.,
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A variational Bayesian approach for frequency diverse non-linear
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ICIP12(2069-2072).
IEEE DOI
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A Novel Sphere-Based Maximum Margin Classification Method,
ICPR14(620-624)
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1412
Breast cancer
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Moftah, H.,
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Hassanien, A.E.,
Schaefer, G.,
Mammary Gland Tumor Detection in Cats Using Ant Colony Optimisation,
ACPR13(942-945)
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1408
ant colony optimisation
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Rajurkar, A.M.,
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Medical image analysis an attempt for mammogram classification using
texture based association rule mining,
NCVPRIPG13(1-5)
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1408
cancer
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Peros, G.,
Zovko-Cihlar, B.,
Comparison of segmentation accuracy for different LUTs applied to
digital mammograms,
WSSIP15(113-116)
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1603
biological tissues
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Efficient presentation of DICOM mammography images using Matlab,
WSSIP08(13-16).
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Code, Mammography.
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Automatic Evaluation System of FISH Images in Breast Cancer,
ICISP14(332-339).
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1406
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A Multiscale Blob Representation of Mammographic Parenchymal Patterns
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CAIP13(II:346-353).
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1311
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Benchmarking Datasets for Breast Cancer Computer-Aided Diagnosis (CADx),
CIARP13(I:326-333).
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1311
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He, W.[Wenda],
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Breast Parenchymal Pattern Analysis in Digital Mammography:
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CAIP13(II:386-393).
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1311
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Meaningful Features for Computerized Detection of Breast Cancer,
CIARP13(II:198-205).
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1311
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A Boosting-Based Approach to Refine the Segmentation of Masses in
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Development of electrochemical biosensor for breast cancer detection
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ICSIPR13(75-81).
IEEE DOI
1304
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1302
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Region based stellate features for classification of mammographic
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ICIP12(2821-2824).
IEEE DOI
1302
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1112
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An automatic and simple breast tumor classification using area matching,
ICIIP11(1-5).
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1112
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Diagnosis of Invasive Ductal Carcinoma using image processing
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ICIIP11(1-6).
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1112
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Classification of abnormalities in digitized mammograms using Extreme
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1106
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Time Reversal Beamforming for Microwave Breast Cancer Detection,
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A Feature Analysis Approach to Mass Detection in Mammography Based on
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Semivariogram Applied for Classification of Benign and Malignant
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0610
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A Method for Interpreting Pixel Grey Levels in Digital Mammography,
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Optimizing the selection of Flip Angle acquisitions for T1 measurement
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0609
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Lee, S.[Sarah],
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Mammogram Analysis Using Two-Dimensional Autoregressive Models:
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CIAP05(900-906).
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The Role of Entropy: Mammogram Analysis,
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0806
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Bornefalk, H.[Hans],
Use of Quadrature Filters for Detection of Stellate Lesions in
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A dynamic fuzzy classifier for detecting abnormalities in mammograms,
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0408
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Christoyianni, I.,
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Automatic Detection of Abnormal Tissue in Mammography,
ICIP01(II: 877-880).
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0108
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McGarry, G.,
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Mammographic Image Segmentation Using a Tissue-mixture Model and Markov
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ICIP00(Vol III: 416-419).
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0008
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Banerjee, A.,
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Tumor Detection in Digital Mammograms,
ICIP00(Vol III: 432-435).
IEEE DOI
0008
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Sari-Sarraf, H., and
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A Novel Approach to Computer-Aided Diagnosis of Mammographic Images,
WACV96(230-235).
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9609
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Morrison, S.[Steven],
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A Model Based Approach to Object Detection in Digital Mammography,
ICIP99(II:182-186).
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9900
Jiang, H.[Hao],
Tiu, W.[Wilson],
Yamamoto, S.[Shinji],
Iisaku, S.I.[Shun-Ichi],
Detection of Spicules in Mammograms,
ICIP97(III: 520-523).
IEEE DOI
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9700
And:
Automatic recognition of spicules in mammograms,
CIAP97(II: 396-403).
Springer DOI
9709
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Marroquin, E.M.,
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Breast Cancer Cell Analysis, Pathology, Nuclei Detection .