21.4.4.2 HEp-2 Cell Analysis, Human Epithelial Type 2 Cells

Li, Y., Shen, L., Yu, S.,
HEp-2 Specimen Image Segmentation and Classification Using Very Deep Fully Convolutional Network,
MedImg(36), No. 7, July 2017, pp. 1561-1572.
IEEE DOI 1707
Computer architecture, Convolution, Feature extraction, Image segmentation, Pattern recognition, Training, Transforms, Cell patterns, classification, fully convolutional network, segmentation BibRef

Lei, H.J.[Hai-Jun], Han, T.[Tao], Zhou, F.[Feng], Yu, Z.[Zhen], Qin, J.[Jing], Elazab, A.[Ahmed], Lei, B.[Baiying],
A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer learning,
PR(79), 2018, pp. 290-302.
Elsevier DOI 1804
HEp-2 cell classification, Residual network, Deeply supervised ResNet, Cross-modal transfer learning BibRef

Shen, L.L.[Lin-Lin], Jia, X.[Xi], Li, Y.X.[Yue-Xiang],
Deep cross residual network for HEp-2 cell staining pattern classification,
PR(82), 2018, pp. 68-78.
Elsevier DOI 1806
Convolutional neural network, Cross connection, Deep cross residual network, HEp-2 classification BibRef

Bates, R., Irving, B., Markelc, B., Kaeppler, J., Brown, G., Muschel, R.J., Brady, S.M., Grau, V., Schnabel, J.A.,
Segmentation of Vasculature From Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images,
MedImg(38), No. 1, January 2019, pp. 1-10.
IEEE DOI 1901
Image segmentation, Tumors, Microscopy, Feature extraction, Graphical models, Labeling, Image segmentation, microscopy BibRef

Al-Milaji, Z.[Zahraa], Ersoy, I.[Ilker], Hafiane, A.[Adel], Palaniappan, K.[Kannappan], Bunyak, F.[Filiz],
Integrating segmentation with deep learning for enhanced classification of epithelial and stromal tissues in H&E images,
PRL(119), 2019, pp. 214-221.
Elsevier DOI 1902
Epithelium and stroma, H&E images, Convolutional neural networks, Segmentation, Classification BibRef

Al-Dulaimi, K.[Khamael], Chandran, V.[Vinod], Nguyen, K.[Kien], Banks, J.[Jasmine], Tomeo-Reyes, I.[Inmaculada],
Benchmarking HEp-2 specimen cells classification using linear discriminant analysis on higher order spectra features of cell shape,
PRL(125), 2019, pp. 534-541.
Elsevier DOI 1909
HEp-2 cell, Classification, Linear discriminant analysis, Higher order spectra, Feature extraction, Benchmarking, Cell shape BibRef

Valkonen, M., Isola, J., Ylinen, O., Muhonen, V., Saxlin, A., Tolonen, T., Nykter, M., Ruusuvuori, P.,
Cytokeratin-Supervised Deep Learning for Automatic Recognition of Epithelial Cells in Breast Cancers Stained for ER, PR, and Ki-67,
MedImg(39), No. 2, February 2020, pp. 534-542.
IEEE DOI 2002
Training, Deep learning, Breast cancer, Tumors, Immune system, Indexes, Convolutional neural networks, Deep learning, image segmentation, digital pathology BibRef

Bajic, B.[Buda], Majtner, T.[Tomáš], Lindblad, J.[Joakim], Sladoje, N.[Nataša],
Generalised deep learning framework for HEp-2 cell recognition using local binary pattern maps,
IET-IPR(14), No. 6, 11 May 2020, pp. 1201-1208.
DOI Link 2005
BibRef

Chandran, M.C.[Manju Chariyamparambil], Jose, M.V.[Marianthiran Victor],
Optimised hybrid classifiers for automatic HEp-2 cell classification,
IET-IPR(14), No. 16, 19 December 2020, pp. 4316-4328.
DOI Link 2103
BibRef

Kumar, D.[Debamita], Maji, P.[Pradipta],
Rough-Bayesian approach to select class-pair specific descriptors for HEp-2 cell staining pattern recognition,
PR(117), 2021, pp. 107982.
Elsevier DOI 2106
HEp-2 cell images, Staining pattern recognition, Texture analysis, Rough sets, Bayes decision theory BibRef

Hradecká, L.[Lucia], Wiesner, D.[David], Sumbal, J.[Jakub], Koledova, Z.S.[Zuzana Sumbalova], Maška, M.[Martin],
Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy,
MedImg(42), No. 1, January 2023, pp. 281-290.
IEEE DOI 2301
Microscopy, Shape, Pipelines, Periodic structures, Mice, Manuals, Bioinformatics, Organoid segmentation, organoid tracking, image synthesis BibRef

Percannella, G.[Gennaro], Petruzzello, U.[Umberto], Ritrovato, P.[Pierluigi], Rundo, L.[Leonardo], Tortorella, F.[Francesco], Vento, M.[Mario],
Joint Intensity Classification and Specimen Segmentation on HEp-2 Images: a Deep Learning Approach,
ICPR22(4343-4349)
IEEE DOI 2212
Deep learning, Training, Image segmentation, Visualization, Target recognition, Transforms, Antibodies BibRef

Jorgensen, B.[Brandon], Al Dulaimi, K.[Khamael], Banks, J.[Jasmine],
HEp-2 Specimen Cell Detection and Classification Using Very Deep Convolutional Neural Networks-Based Cell Shape,
DICTA21(01-06)
IEEE DOI 2201
Training, Image segmentation, Pathology, Recurrent neural networks, Shape, Level set, Convolutional neural networks, cell shape BibRef

Majtner, T.[Tomáš],
HEp-2 Cell Image Recognition with Transferable Cross-Dataset Synthetic Samples,
CAIP21(I:215-225).
Springer DOI 2112
BibRef

Shephard, A.J.[Adam J.], Graham, S.[Simon], Bashir, R.M.S.[R. M. Saad], Jahanifar, M.[Mostafa], Mahmood, H.[Hanya], Khurram, S.A.[Syed Ali], Rajpoot, N.M.[Nasir M.],
Simultaneous Nuclear Instance and Layer Segmentation in Oral Epithelial Dysplasia,
CDPath21(552-561)
IEEE DOI 2112
Deep learning, Pathology, Costs, Semantics, Computer architecture BibRef

Gupta, K.[Krati], Bhavsar, A.[Arnav], Sao, A.K.[Anil K.],
A CNN Based HEp-2 Specimen Image Segmentation and Identification of Mitotic Spindle Type Specimens,
CAIP19(I:564-575).
Springer DOI 1909
BibRef

Majtner, T.[Tomáš], Bajic, B.[Buda], Lindblad, J.[Joakim], Sladoje, N.[Nataša], Blanes-Vidal, V.[Victoria], Nadimi, E.S.[Esmaeil S.],
On the Effectiveness of Generative Adversarial Networks as HEp-2 Image Augmentation Tool,
SCIA19(439-451).
Springer DOI 1906
BibRef

Funke, J.[Jan], Mais, L.[Lisa], Champion, A.[Andrew], Dye, N.[Natalie], Kainmueller, D.[Dagmar],
A Benchmark for Epithelial Cell Tracking,
BioIm18(VI:437-445).
Springer DOI 1905
BibRef

Atienza, N., Escudero, L.M., Jimenez, M.J., Soriano-Trigueros, M.,
Characterising Epithelial Tissues Using Persistent Entropy,
CTIC19(179-190).
Springer DOI 1901
BibRef

Xie, H., He, Y., Lei, H., Han, T., Yu, Z., Lei, B.,
Deeply Supervised Residual Network for HEp-2 Cell Classification,
ICPR18(699-703)
IEEE DOI 1812
Training, Feature extraction, Computer architecture, Convergence, Microprocessors, Task analysis, Optimization, Deeply supervised ResNet (DSRN) BibRef

Khoshdeli, M., Winkelmaier, G., Parvin, B.,
Multilayer Encoder-Decoder Network for 3D Nuclear Segmentation in Spheroid Models of Human Mammary Epithelial Cell Lines,
Microscopy18(2320-23206)
IEEE DOI 1812
Computer architecture, Microprocessors, Image segmentation, Convolution, Solid modeling, volumetric convolution BibRef

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 BibRef

Tavares Vieira, R., Negri, T., Cavichiolli, A., Gonzaga, A.,
Human Epithelial Type 2 (HEp-2) Cell Classification by Using a Multiresolution Texture Descriptor,
WVC17(1-6)
IEEE DOI 1804
biomedical optical imaging, cellular biophysics, diseases, feature extraction, fluorescence, image classification, texture BibRef

Rodrigues, L.F., Naldi, M.C., Mari, J.F.,
HEp-2 Cell Image Classification Based on Convolutional Neural Networks,
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 BibRef

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, Pattern recognition, 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, Pattern recognition, Radio frequency, Support vector machines, Training, Vegetation BibRef

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, Pattern recognition, Principal component analysis, Training BibRef

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, Pattern recognition, Testing, Training, CNN, Hep-2, class-balanced, classification BibRef

Al-Dulaimi, K., Nguyen, K., Banks, J., Chandran, V., Tomeo-Reyes, I.,
Classification of White Blood Cells Using L-Moments Invariant Features of Nuclei Shape,
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 .

Last update:Sep 28, 2024 at 17:47:54