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HEp-2 cell classification
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Indirect immunofluorescence
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Computer architecture, Convolution, Feature extraction,
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Image segmentation, Tumors, Microscopy, Feature extraction,
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Epithelium and stroma, H&E images,
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1909
HEp-2 cell, Classification, Linear discriminant analysis,
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Cytokeratin-Supervised Deep Learning for Automatic Recognition of
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IEEE DOI
2002
Training, Deep learning, Breast cancer, Tumors, Immune system, Indexes,
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2106
HEp-2 cell images, Staining pattern recognition,
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Microscopy, Shape, Pipelines, Periodic structures, Mice, Manuals,
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Training, Image segmentation, Pathology, Recurrent neural networks,
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Khurram, S.A.[Syed Ali],
Rajpoot, N.M.[Nasir M.],
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CDPath21(552-561)
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2112
Deep learning, Pathology, Costs,
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1909
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Majtner, T.[Tomáš],
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1905
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ICPR18(699-703)
IEEE DOI
1812
Training, Feature extraction, Computer architecture, Convergence,
Microprocessors, Task analysis, Optimization,
Deeply supervised ResNet (DSRN)
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Khoshdeli, M.,
Winkelmaier, G.,
Parvin, B.,
Multilayer Encoder-Decoder Network for 3D Nuclear Segmentation in
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IEEE DOI
1812
Computer architecture,
Microprocessors, Image segmentation, Convolution, Solid modeling,
volumetric convolution
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Kastaniotis, D.,
Ntinou, I.,
Tsourounis, D.,
Economou, G.,
Fotopoulos, S.,
Attention-Aware Generative Adversarial Networks (ATA-GANs),
IVMSP18(1-5)
IEEE DOI
1809
Cams, Training, Generators, Task analysis,
Generative adversarial networks, Biological system modeling,
HEp-2 cells
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Tavares Vieira, R.,
Negri, T.,
Cavichiolli, A.,
Gonzaga, A.,
Human Epithelial Type 2 (HEp-2) Cell Classification by Using a
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WVC17(1-6)
IEEE DOI
1804
biomedical optical imaging, cellular biophysics, diseases,
feature extraction, fluorescence, image classification,
texture
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Rodrigues, L.F.,
Naldi, M.C.,
Mari, J.F.,
HEp-2 Cell Image Classification Based on Convolutional Neural
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WVC17(13-18)
IEEE DOI
1804
cellular biophysics, convergence, diseases, image classification,
learning (artificial intelligence), medical image processing,
staining patterns classification
BibRef
Li, H.W.[Hong-Wei],
Huang, H.[Hao],
Zheng, W.S.,
Xie, X.H.[Xiao-Hua],
Zhang, J.,
HEp-2 specimen classification via deep CNNs and pattern histogram,
ICPR16(2145-2149)
IEEE DOI
1705
Convolution, Feature extraction, Histograms, Image segmentation,
Neural networks, Support vector machines, Training
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Li, Y.X.[Yue-Xiang],
Shen, L.,
Zhou, X.D.[Xian-De],
Yu, S.Q.[Shi-Qi],
HEp-2 specimen classification with fully convolutional network,
ICPR16(96-100)
IEEE DOI
1705
Computer architecture, Diseases, Feature extraction,
Image segmentation, Microprocessors, Training,
cell patterns, classification, fully convolutional network, segmentation
BibRef
Prasath, V.B.S.,
Kassim, Y.M.,
Oraibi, Z.A.,
Guiriec, J.B.,
Hafiane, A.,
Seetharaman, G.,
Palaniappan, K.,
HEp-2 cell classification and segmentation using motif texture
patterns and spatial features with random forests,
ICPR16(90-95)
IEEE DOI
1705
Image segmentation, Imaging, Radio frequency,
Support vector machines, Training, Vegetation
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BS, D.[Divya],
Subramaniam, K.,
HR, N.[Nanjundaswamy],
HEp-2 cell classification using artificial neural network approach,
ICPR16(84-89)
IEEE DOI
1705
Artificial neural networks, Classification algorithms,
Feature extraction, Histograms,
Principal component analysis, Training
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Jia, X.[Xi],
Shen, L.L.[Lin-Lin],
Zhou, X.[Xiande],
Yu, S.Q.[Shi-Qi],
Deep convolutional neural network based HEp-2 cell classification,
ICPR16(77-80)
IEEE DOI
1705
Computer architecture, Feature extraction, Immune system,
Microprocessors, Testing, Training, CNN, Hep-2,
class-balanced, classification
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Al-Dulaimi, K.,
Nguyen, K.,
Banks, J.,
Chandran, V.,
Tomeo-Reyes, I.,
Classification of White Blood Cells Using L-Moments Invariant
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IVCNZ18(1-6)
IEEE DOI
1902
Feature extraction, Shape, Databases, Radon, White blood cells,
Image segmentation, Support vector machines, Classification,
Support Vector Machines
BibRef
Al-Dulaimi, K.,
Banks, J.,
Tomeo-Reyes, I.,
Chandran, V.,
Automatic segmentation of HEp-2 cell Fluorescence microscope images
using level set method via geometric active contours,
ICPR16(81-83)
IEEE DOI
1705
BibRef
And: A1, A3, A2, A4:
White Blood Cell Nuclei Segmentation Using Level Set Methods and
Geometric Active Contours,
DICTA16(1-7)
IEEE DOI
1701
Active contours, Fluorescence, Image segmentation, Immune system,
Level set, MATLAB, Morphological operations,
HEp-2 cell segmentation, geometric active contours, level, set, method
Active contours
BibRef
Zhou, X.D.[Xian-De],
Li, Y.X.[Yue-Xiang],
Wu, W.F.[Wen-Feng],
Shen, L.L.[Lin-Lin],
HEP-2 cell image classification using local features and K-means
clustering based joint sparse representation,
ICWAPR16(179-183)
IEEE DOI
1611
Detectors
BibRef
Anam, A.M.,
Rushdi, M.A.,
Fahmy, A.S.,
Enhancement of low-resolution HEp-2 cell image classification using
partial least-square regression,
ICIP16(1245-1249)
IEEE DOI
1610
Correlation
BibRef
Miros, A.,
Wiliem, A.,
Holohan, K.,
Ball, L.,
Hobson, P.,
Lovell, B.C.,
A Benchmarking Platform for Mitotic Cell Classification of ANA IIF
HEp-2 Images,
DICTA15(1-6)
IEEE DOI
1603
diseases
BibRef
Gupta, K.[Krati],
Gupta, V.[Vibha],
Bhavsar, A.[Arnav],
Sao, A.K.[Anil K.],
Class-specific hierarchical classification for HEP-2 specimen images,
ICIP15(641-645)
IEEE DOI
1512
Class-specific features; Hierarchical framework; Specimen-level images
BibRef
Jiang, X.Y.[Xiao-Yi],
Percannella, G.[Gennaro],
Vento, M.[Mario],
A Verification-Based Multithreshold Probing Approach to HEp-2 Cell
Segmentation,
CAIP15(II:266-276).
Springer DOI
1511
BibRef
Zhao, Y.[Yan],
Gao, Z.M.[Zhi-Min],
Wang, L.[Lei],
Zhou, L.P.[Lu-Ping],
Experimental Study of Unsupervised Feature Learning for HEp-2 Cell
Images Clustering,
DICTA14(1-8)
IEEE DOI
1502
feature extraction
BibRef
Ensafi, S.[Shahab],
Lu, S.J.[Shi-Jian],
Kassim, A.A.[Ashraf A.],
Tan, C.L.[Chew Lim],
Automatic CAD System for HEp-2 Cell Image Classification,
ICPR14(3321-3326)
IEEE DOI
1412
Accuracy
BibRef
Fakhrzadeh, A.[Azadeh],
Spörndly-Nees, E.[Ellinor],
Epithelial Cell Segmentation in Histological Images of Testicular
Tissue Using Graph-Cut,
CIAP13(II:201-208).
Springer DOI
1309
BibRef
Fakhrzadeh, A.[Azadeh],
Spörndly-Nees, E.[Ellinor],
Holm, L.,
Hendriks, C.L.L.,
Analyzing Tubular Tissue in Histopathological Thin Sections,
DICTA12(1-6).
IEEE DOI
1303
BibRef
Doshi, N.P.[Niraj P.],
Schaefer, G.[Gerald],
Automatic Classification of HEp-2 Cells Using Multi-dimensional Local
Binary Patterns,
ACPR13(293-297)
IEEE DOI
1408
diseases
See also comprehensive benchmark of local binary pattern algorithms for texture retrieval, A.
BibRef
Doshi, N.P.,
Schaefer, G.,
Texture Classification Using Multi-dimensional LBP Variance,
ACPR13(672-676)
IEEE DOI
1408
computer vision
BibRef
Schaefer, G.,
Doshi, N.P.,
Krawczyk, B.,
HEp-2 Cell Classification Using Multi-dimensional Local Binary
Patterns and Ensemble Classification,
ACPR13(951-955)
IEEE DOI
1408
feature selection
BibRef
Iannello, G.[Giulio],
Onofri, L.[Leonardo],
Soda, P.[Paolo],
A Slightly Supervised Approach for Positive/Negative Classification of
Fluorescence Intensity in HEp-2 Images,
CIAP13(II:319-328).
Springer DOI
1309
BibRef
Soda, P.[Paolo],
Iannello, G.[Giulio],
A Hybrid Multi-Expert Systems for HEp-2 Staining Pattern Classification,
CIAP07(685-690).
IEEE DOI
0709
BibRef
Ersoy, I.,
Bunyak, F.,
Peng, J.,
Palaniappan, K.,
HEp-2 cell classification in IIF images using Shareboost,
ICPR12(3362-3365).
WWW Link.
1302
BibRef
Ghosh, S.[Subarna],
Chaudhary, V.[Vipin],
Feature analysis for automatic classification of HEp-2 florescence
patterns: Computer-Aided Diagnosis of Auto-immune diseases,
ICPR12(174-177).
WWW Link.
1302
BibRef
Li, K.[Kuan],
Yin, J.P.[Jian-Ping],
Lu, Z.[Zhi],
Kong, X.F.[Xiang-Fei],
Zhang, R.[Rui],
Liu, W.Y.[Wen-Yin],
Multiclass boosting SVM using different texture features in HEp-2 cell
staining pattern classification,
ICPR12(170-173).
WWW Link.
1302
BibRef
Strandmark, P.[Petter],
Ulen, J.[Johannes],
Kahl, F.[Fredrik],
HEp-2 staining pattern classification,
ICPR12(33-36).
WWW Link.
1302
BibRef
Mazo, C.[Claudia],
Trujillo, M.[Maria],
Salazar, L.[Liliana],
Automatic Classification of Coating Epithelial Tissue,
CIARP14(311-318).
Springer DOI
1411
BibRef
Earlier:
An Automatic Segmentation Approach of Epithelial Cells Nuclei,
CIARP12(567-574).
Springer DOI
1209
BibRef
Wiliem, A.,
Hobson, P.,
Minchin, R.F.,
Lovell, B.C.,
An Automatic Image Based Single Dilution Method for End Point Titre
Quantitation of Antinuclear Antibodies Tests Using HEp-2 Cells,
DICTA11(1-6).
IEEE DOI
1205
BibRef
Perner, P.,
Perner, H.,
Muller, B.,
Texture classification based on the boolean model and its application
to HEP-2 cells,
ICPR02(II: 406-409).
IEEE DOI
0211
BibRef
Perner, P.[Petra],
Verification of Hypotheses Generated by Case-Based Reasoning Object
Matching,
IWCIA17(66-78).
Springer DOI
1706
BibRef
Earlier:
Model Development and Incremental Learning Based on Case-Based
Reasoning for Signal and Image Analysis,
CompIMAGE16(3-24).
Springer DOI
1704
BibRef
Earlier:
Case-Based Object Recognition with Application to Biological Images,
CIARP06(27-37).
Springer DOI
0611
See also Knowledge-Based Image-Inspection System for Automatic Defect Recognition, Classification, and Process Diagnosis, A.
BibRef
Perner, P.[Petra],
Image Analysis and Classification of HEp-2 Cells in Fluorescent Images,
ICPR98(Vol II: 1677-1679).
IEEE DOI
9808
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
HIV, HIV/AIDS Cell Analysis .