21.13.4 Medical Applications -- Cervical Cancer Analysis, Ovarian Cancer

Chapter Contents (Back)
Cervical Cancer. Ovarian Cancer. Cancer Detection.

Oliver, L.H., Poulsen, R.S., Toussaint, G.T., Louis, C.,
Classification of atypical cells in the automatic cytoscreening for cervical cancer,
PR(11), No. 3, 1979, pp. 205-212.
Elsevier DOI 0309
BibRef

Bengtsson, E., Eriksson, O., Holmquist, J., Jarkrans, T., Nordin, B., Stenkvist, B.,
Segmentation of Cervical Cells: Detection of Overlapping Cell Nuclei,
CGIP(16), No. 4, August 1981, pp. 382-394.
Elsevier DOI BibRef 8108

Goerttler, K., Stoehr, M., Ploem, J., Bloss, W.H.,
Requirements for Sequential Flow and Static Image Analysis: A Preliminary Study,
PR(13), No. 4, 1981, pp. 279-283.
Elsevier DOI 0309
The hybridization of flow and image analysis in the prescreening of gynecological smears combines the advantages of both methods. BibRef

Zahniser, D.J.,
Automation of Pap Smear Analysis: A Review and Status Report,
PDA83(265-294). BibRef 8300

Lin, Y.K., and Fu, K.S.,
Automatic Classification of Cervical Cells Using a Binary Tree Classifier,
PR(16), No. 1, 1983, pp. 69-80.
Elsevier DOI BibRef 8300

Nguyen, N.G., Poulsen, R.S., Louis, C.,
Some New Color Features and Their Application to Cervical Cell Classification,
PR(16), No. 4, 1983, pp. 401-411.
Elsevier DOI BibRef 8300

Ji, Q.A.[Qi-Ang], Engel, J.[John], Craine, E.[Eric],
Classifying cervix tissue patterns with texture analysis,
PR(33), No. 9, September 2000, pp. 1561-1573.
Elsevier DOI 0005
BibRef

Ji, Q., Engel, J., Craine, E.,
Texture analysis for classification of cervix lesions,
MedImg(19), No. 11, November 2000, pp. 1144-1149.
IEEE Top Reference. 0110
BibRef

Potonik, B.[Boidar], Zazula, D.[Damjan],
Automated analysis of a sequence of ovarian ultrasound images. Part I: Segmentation of single 2D images,
IVC(20), No. 3, March 2002, pp. 217-225.
Elsevier DOI 0202
BibRef

Potonik, B.[Boidar], Zazula, D.[Damjan],
Automated analysis of a sequence of ovarian ultrasound images. Part II: Prediction-based object recognition from a sequence of images,
IVC(20), No. 3, March 2002, pp. 227-235.
Elsevier DOI 0202
BibRef

Luck, B.L., Carlson, K.D., Bovik, A.C., Richards-Kortum, R.R.,
An Image Model and Segmentation Algorithm for Reflectance Confocal Images of In Vivo Cervical Tissue,
IP(14), No. 9, September 2005, pp. 1265-1276.
IEEE DOI 0508
BibRef

Braumann, U.D., Kuska, J.P., Einenkel, J., Horn, L.C., Loffler, M., Hockel, M.,
Three-Dimensional Reconstruction and Quantification of Cervical Carcinoma Invasion Fronts From Histological Serial Sections,
MedImg(24), No. 10, October 2005, pp. 1286-1307.
IEEE DOI 0510
BibRef

Gavião, W.[Wilson], Scharcanski, J.[Jacob],
Evaluating the mid-secretory endometrium appearance using hysteroscopic digital video summarization,
IVC(25), No. 1, January 2007, pp. 70-77.
Elsevier DOI 0611
Medical image analysis; Video summarization; Hysteroscopies; Gynecology BibRef

Scharcanski, J., Gaviao, W.,
Hierarchical Summarization of Diagnostic Hysteroscopy Videos,
ICIP06(129-132).
IEEE DOI 0610
BibRef

Yang-Mao, S.F.[Shys-Fan], Chan, Y.K., Chu, Y.P.,
Edge Enhancement Nucleus and Cytoplast Contour Detector of Cervical Smear Images,
SMC-B(37), No. 2, April 2007, pp. 353-366.
IEEE DOI 0803
BibRef

Tsai, M.H.[Meng-Husiun], Chan, Y.K.[Yung-Kuan], Lin, Z.Z.[Zhe-Zheng], Yang-Mao, S.F.[Shys-Fan], Huang, P.C.[Po-Chi],
Nucleus and cytoplast contour detector of cervical smear image,
PRL(29), No. 9, 1 July 2008, pp. 1441-1453.
Elsevier DOI 0711
Cervical smear screening; Cervical cancer; Image segmentation; Salt and pepper noise; Gaussian noise; Contour detection BibRef

Lin, C.H.[Chuen-Horng], Chan, Y.K.[Yung-Kuan], Chen, C.C.[Chun-Chieh],
Detection and segmentation of cervical cell cytoplast and nucleus,
IJIST(19), No. 3, September 2009, pp. 260-270.
DOI Link 0909
BibRef

Staring, M., van der Heide, U.A., Klein, S., Viergever, M.A., Pluim, J.P.W.,
Registration of Cervical MRI Using Multifeature Mutual Information,
MedImg(28), No. 9, September 2009, pp. 1412-1421.
IEEE DOI 0909
BibRef

Langerak, T.R., van der Heide, U.A., Kotte, A.N.T.J., Viergever, M.A., van Vulpen, M., Pluim, J.P.W.,
Label Fusion in Atlas-Based Segmentation Using a Selective and Iterative Method for Performance Level Estimation (SIMPLE),
MedImg(29), No. 12, December 2010, pp. 2000-2008.
IEEE DOI 1101
BibRef

Langerak, T.R., van der Heide, U.A., Kotte, A.N.T.J., Berendsen, F.F., Pluim, J.P.W.,
Improving label fusion in multi-atlas based segmentation by locally combining atlas selection and performance estimation,
CVIU(130), No. 1, 2015, pp. 71-79.
Elsevier DOI 1411
Atlas-based segmentation BibRef

Greenspan, H., Gordon, S., Zimmerman, G., Lotenberg, S., Jeronimo, J.[Jose], Antani, S.[Sameer], Long, L.R.[L. Rodney],
Automatic Detection of Anatomical Landmarks in Uterine Cervix Images,
MedImg(28), No. 3, March 2009, pp. 454-468.
IEEE DOI 0903
BibRef

Rahman, M.M.[M. Mahmudur], Antani, S.K.[Sameer K.], Thoma, G.R.[George R.],
Biomedical Image Retrieval in a Fuzzy Feature Space with Affine Region Detection and Vector Quantization of a Scale-Invariant Descriptor,
ISVC10(III: 261-270).
Springer DOI 1011
BibRef

Xue, Z.Y.[Zhi-Yun], Long, L.R.[L. Rodney], Antani, S.K.[Sameer K.], Thoma, G.R.[George R.], Jeronimo, J.[Jose],
Cervicographic image retrieval by spatial similarity of lesions,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Alush, A.[Amir], Greenspan, H., Goldberger, J.[Jacob],
Automated and Interactive Lesion Detection and Segmentation in Uterine Cervix Images,
MedImg(29), No. 2, February 2010, pp. 488-501.
IEEE DOI 1002
BibRef

Gordon, S.[Shiri], Greenspan, H.[Hayit],
An agglomerative segmentation framework for non-convex regions within uterine cervix images,
IVC(28), No. 12, December 2010, pp. 1682-1701.
Elsevier DOI 1003
Medical image analysis; Image segmentation; Graph cuts; Cervical cancer; Cervicography images BibRef

Park, S.Y., Sargent, D., Lieberman, R., Gustafsson, U.,
Domain-Specific Image Analysis for Cervical Neoplasia Detection Based on Conditional Random Fields,
MedImg(30), No. 3, March 2011, pp. 867-878.
IEEE DOI 1103
BibRef

Li, K.[Kuan], Lu, Z.[Zhi], Liu, W.Y.[Wen-Yin], Yin, J.P.[Jian-Ping],
Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake,
PR(45), No. 4, April 2012, pp. 1255-1264.
Elsevier DOI 1112
Cervical cell; Boundary extraction; Radiating gradient vector flow; Active contour BibRef

Lu, C.[Chao], Chelikani, S., Jaffray, D.A., Milosevic, M.F., Staib, L.H., Duncan, J.S.,
Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy,
MedImg(31), No. 6, June 2012, pp. 1213-1227.
IEEE DOI 1206
BibRef

Gençtav, A.[Asli], Aksoy, S.[Selim], Önder, S.[Sevgen],
Unsupervised segmentation and classification of cervical cell images,
PR(45), No. 12, December 2012, pp. 4151-4168.
Elsevier DOI 1208
Pap smear test; Cell grading; Automatic thresholding; Hierarchical segmentation; Multi-scale segmentation; Hierarchical clustering; Ranking; Optimal leaf ordering BibRef

Steger, S., Bozoglu, N., Kuijper, A., Wesarg, S.,
Application of Radial Ray Based Segmentation to Cervical Lymph Nodes in CT Images,
MedImg(32), No. 5, May 2013, pp. 888-900.
IEEE DOI 1305
BibRef

Berendsen, F.F.[Floris F.], van der Heide, U.A.[Uulke A.], Langerak, T.R.[Thomas R.], Kotte, A.N.T.J.[Alexis N.T.J.], Pluim, J.P.W.[Josien P.W.],
Free-form image registration regularized by a statistical shape model: application to organ segmentation in cervical MR,
CVIU(117), No. 9, 2013, pp. 1119-1127.
Elsevier DOI 1307
Inter-subject BibRef

Hiary, H.[Hazem], Alomari, R.S.[Raja S.], Chaudhary, V.[Vipin],
Segmentation and localisation of whole slide images using unsupervised learning,
IET-IPR(7), No. 5, 2013, pp. 464-471.
DOI Link 1310
BibRef

Hiary, H.[Hazem], Alomari, R.S.[Raja S.], Saadah, M.[Maha], Chaudhary, V.[Vipin],
Automated segmentation of stromal tissue in histology images using a voting Bayesian model,
SIViP(7), No. 6, November 2013, pp. 1229-1237.
Springer DOI 1310
BibRef

Torheim, T., Malinen, E., Kvaal, K., Lyng, H., Indahl, U.G., Andersen, E.K.F., Futsaether, C.M.,
Classification of Dynamic Contrast Enhanced MR Images of Cervical Cancers Using Texture Analysis and Support Vector Machines,
MedImg(33), No. 8, August 2014, pp. 1648-1656.
IEEE DOI 1408
Accuracy BibRef

Cengizler, C.[Caglar], Guven, M.[Mustafa], Avci, M.[Mutlu],
A fluid dynamics-based deformable model for segmentation of cervical cell images,
SIViP(8), No. S1, December 2014, pp. 21-32.
WWW Link. 1411
BibRef

Song, D.Z.[De-Zhao], Kim, E., Huang, X.L.[Xiao-Lei], Patruno, J., Munoz-Avila, H., Heflin, J., Long, L.R., Antani, S.,
Multimodal Entity Coreference for Cervical Dysplasia Diagnosis,
MedImg(34), No. 1, January 2015, pp. 229-245.
IEEE DOI 1502
biological organs BibRef

Lu, Z.[Zhi], Carneiro, G., Bradley, A.P.,
An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells,
IP(24), No. 4, April 2015, pp. 1261-1272.
IEEE DOI 1503
biomedical optical imaging BibRef

Lindblad, J.[Joakim], Bengtsson, E.[Ewert], Sladoje, N.[Nataša],
Microscopy Image Enhancement for Cost-Effective Cervical Cancer Screening,
SCIA15(440-451).
Springer DOI 1506
BibRef

Tan, M., Li, Z., Qiu, Y., McMeekin, S.D., Thai, T.C., Ding, K., Moore, K.N., Liu, H., Zheng, B.,
A New Approach to Evaluate Drug Treatment Response of Ovarian Cancer Patients Based on Deformable Image Registration,
MedImg(35), No. 1, January 2016, pp. 316-325.
IEEE DOI 1601
Cancer BibRef

Zhang, J.W.[Jian-Wei], Hu, Z.P.[Zhen-Peng], Han, G.Q.[Guo-Qiang], He, X.Z.[Xiao-Zhen],
Segmentation of overlapping cells in cervical smears based on spatial relationship and Overlapping Translucency Light Transmission Model,
PR(60), No. 1, 2016, pp. 286-295.
Elsevier DOI 1609
Cervical overlapping cell BibRef

Xu, T.[Tao], Zhang, H.[Han], Xin, C.[Cheng], Kim, E.[Edward], Long, L.R.[L. Rodney], Xue, Z.Y.[Zhi-Yun], Antani, S.[Sameer], Huang, X.L.[Xiao-Lei],
Multi-feature based benchmark for cervical dysplasia classification evaluation,
PR(63), No. 1, 2017, pp. 468-475.
Elsevier DOI 1612
Cervical cancer screening BibRef

Song, Y.Y.[You-Yi], Tan, E.L.[Ee-Leng], Jiang, X.D.[Xu-Dong], Cheng, J.Z.[Jie-Zhi], Ni, D.[Dong], Chen, S.P.[Si-Ping], Lei, B.Y.[Bai-Ying], Wang, T.F.[Tian-Fu],
Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images,
MedImg(36), No. 1, January 2017, pp. 288-300.
IEEE DOI 1701
BibRef
And: Corrections: MedImg(38), No. 6, June 2019, pp. 1543-1543.
IEEE DOI 1906
Cervical cancer BibRef

Cunningham, R.J., Harding, P.J., Loram, I.D.,
Real-Time Ultrasound Segmentation, Analysis and Visualisation of Deep Cervical Muscle Structure,
MedImg(36), No. 2, February 2017, pp. 653-665.
IEEE DOI 1702
Image segmentation BibRef

Zhao, L.[Lili], Li, K.[Kuan], Yin, J.P.[Jian-Ping], Liu, Q.[Qiang], Wang, S.Q.[Si-Qi],
Complete three-phase detection framework for identifying abnormal cervical cells,
IET-IPR(11), No. 4, April 2017, pp. 258-265.
DOI Link 1704
BibRef

Cigale, B.[Boris], Zazula, D.[Damjan],
Directional 3D Wavelet Transform Based on Gaussian Mixtures for the Analysis of 3D Ultrasound Ovarian Volumes,
PAMI(41), No. 1, January 2019, pp. 64-77.
IEEE DOI 1812
Ultrasonic imaging, Wavelet transforms, Manuals, 3D ultrasonic imaging BibRef

Arya, M.[Mithlesh], Mittal, N.[Namita], Singh, G.[Girdhari],
Texture-based feature extraction of smear images for the detection of cervical cancer,
IET-CV(12), No. 8, December 2018, pp. 1049-1059.
DOI Link 1812
BibRef

Zhang, X.Q.[Xiao-Qing], Zhao, S.G.[Shu-Guang],
Cervical image classification based on image segmentation preprocessing and a CapsNet network model,
IJIST(29), No. 1, March 2019, pp. 19-28.
WWW Link. 1902
BibRef

Rigaud, B., Simon, A., Gobeli, M., Leseur, J., Duvergé, L., Williaume, D., Castelli, J., Lafond, C., Acosta, O., Haigron, P., de Crevoisier, R.,
Statistical Shape Model to Generate a Planning Library for Cervical Adaptive Radiotherapy,
MedImg(38), No. 2, February 2019, pp. 406-416.
IEEE DOI 1902
Shape, Planning, Libraries, Bladder, Strain, Deformable models, Computed tomography, Radiotherapy, cervical, planning library BibRef

Zhang, J.W.[Jian-Wei], He, J.T.[Jun-Ting], Chen, T.F.[Tian-Fu], Liu, Z.M.[Zhen-Mei], Chen, D.N.[Dan-Ni],
Abnormal region detection in cervical smear images based on fully convolutional network,
IET-IPR(13), No. 4, March 2019, pp. 583-590.
DOI Link 1903
BibRef

He, D.Y.[Dong-Yun], Liu, L.[Li], Miao, S.[Sheng], Tong, X.L.[Xiao-Li], Sheng, M.J.[Min-Jia],
Probabilistic guided polycystic ovary syndrome recognition using learned quality kernel,
JVCIR(63), 2019, pp. 102587.
Elsevier DOI 1909
Probabilistic model, Image quality assessment, Image recognition BibRef

Song, Y., Zhu, L., Qin, J., Lei, B., Sheng, B., Choi, K.,
Segmentation of Overlapping Cytoplasm in Cervical Smear Images via Adaptive Shape Priors Extracted From Contour Fragments,
MedImg(38), No. 12, December 2019, pp. 2849-2862.
IEEE DOI 1912
Shape, Image segmentation, Task analysis, Feature extraction, Cervical cancer, Level set, Overlapping cytoplasm segmentation, automatic cervical cancer screening BibRef

Ramasamy, R.[Raghupathy], Chinnasamy, C.[Chitra],
Detection and segmentation of cancer regions in cervical images using fuzzy logic and adaptive neuro fuzzy inference system classification method,
IJIST(30), No. 2, 2020, pp. 412-420.
DOI Link 2005
cancer, cervical, classifications, fuzzy logic, segmentation BibRef

Li, Y., Chen, J., Xue, P., Tang, C., Chang, J., Chu, C., Ma, K., Li, Q., Zheng, Y., Qiao, Y.,
Computer-Aided Cervical Cancer Diagnosis Using Time-Lapsed Colposcopic Images,
MedImg(39), No. 11, November 2020, pp. 3403-3415.
IEEE DOI 2011
Machine learning, Feature extraction, Cervical cancer, Lesions, Medical diagnostic imaging, Cervical cancer, acetic acid test, feature fusion BibRef

Somasundaram, D.[Devaraj], Gnanasaravanan, S.[Subramaniam], Madian, N.[Nirmala],
Automatic segmentation of nuclei from pap smear cell images: A step toward cervical cancer screening,
IJIST(30), No. 4, 2020, pp. 1209-1219.
DOI Link 2011
multithresholding, nucleus and cytoplasm, pap smear, SVM classifiers BibRef

Daly, A.[Asma], Yazid, H.[Hedi], Solaiman, B.[Basel], Ben Amara, N.E.[Najoua Essoukri],
Multiatlas-based segmentation of female pelvic organs: Application for computer-aided diagnosis of cervical cancer,
IJIST(31), No. 1, 2021, pp. 302-312.
DOI Link 2102
atlas-based segmentation, cervical cancer, female pelvic organs, multiatlas-based segmentation, online machine learning BibRef

Ma, D.Y.[Dong-Yang], Liu, J.H.[Jin-Hua], Li, J.[Jing], Zhou, Y.F.[Yuan-Feng],
Cervical cancer detection in cervical smear images using deep pyramid inference with refinement and spatial-aware booster,
IET-IPR(14), No. 17, 24 December 2020, pp. 4717-4725.
DOI Link 2104
BibRef

Hao, D.M.[Dong-Mei], Song, X.X.[Xiao-Xiao], Qiu, Q.[Qian], Xin, X.[Xin], Yang, L.[Lin], Liu, X.H.[Xiao-Hong], Jiang, H.Q.[Hong-Qing], Zheng, D.C.[Ding-Chang],
Effect of electrode configuration on recognizing uterine contraction with electrohysterogram: Analysis using a convolutional neural network,
IJIST(31), No. 2, 2021, pp. 972-980.
DOI Link 2105
convolutional neural network, electrode configuration, electrohysterogram, uterine contraction BibRef

Meng, Z.[Zhu], Zhao, Z.C.[Zhi-Cheng], Li, B.Y.[Bing-Yang], Su, F.[Fei], Guo, L.[Limei],
A Cervical Histopathology Dataset for Computer Aided Diagnosis of Precancerous Lesions,
MedImg(40), No. 6, June 2021, pp. 1531-1541.
IEEE DOI 2106
Histopathology, Lesions, Cancer, Annotations, Supervised learning, Feature extraction, Image segmentation, Cervical histopathology, weakly supervised learning BibRef

Dong, Y.[Yang], Wan, J.C.[Jia-Chen], Wang, X.J.[Xing-Jian], Xue, J.H.[Jing-Hao], Zou, J.[Jibin], He, H.H.[Hong-Hui], Li, P.C.[Peng-Cheng], Hou, A.[Anli], Ma, H.[Hui],
A Polarization-Imaging-Based Machine Learning Framework for Quantitative Pathological Diagnosis of Cervical Precancerous Lesions,
MedImg(40), No. 12, December 2021, pp. 3728-3738.
IEEE DOI 2112
Pathology, Imaging, Lesions, Microscopy, Image segmentation, Machine learning, Task analysis, Cervical precancerous tissues, quantitative pathological diagnosis BibRef

Su, L.[Limei], Huang, S.J.[Shen-Jiao], Wang, Z.Y.[Zheng-Yin], Zhang, Z.Q.[Zhi-Qin], Wei, H.J.[Hua-Jiang], Chen, T.S.[Tong-Sheng],
Whole slide cervical image classification based on convolutional neural network and random forest,
IJIST(32), No. 3, 2022, pp. 767-777.
DOI Link 2205
cervical cancer, convolutional neural network, multilevel feature fusion, principal component analysis, whole slide cervical image BibRef

Liu, W.L.[Wan-Li], Li, C.[Chen], Xu, N.[Ning], Jiang, T.[Tao], Rahaman, M.M.[Md Mamunur], Sun, H.Z.[Hong-Zan], Wu, X.C.[Xiang-Chen], Hu, W.M.[Wei-Ming], Chen, H.Y.[Hao-Yuan], Sun, C.H.[Chang-Hao], Yao, Y.D.[Yu-Dong], Grzegorzek, M.[Marcin],
CVM-Cervix: A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron,
PR(130), 2022, pp. 108829.
Elsevier DOI 2206
Convolutional neural network, Visual transformer, Multilayer perceptron, Cervical cell classification, Image classification BibRef

Mahyari, T.L.[Tayebeh Lotfi], Dansereau, R.M.[Richard M.],
Multi-layer random walker image segmentation for overlapped cervical cells using probabilistic deep learning methods,
IET-IPR(16), No. 11, 2022, pp. 2959-2972.
DOI Link 2208
BibRef

Bijoy, M.B., Akondi, S.M.[Sai Manoj], Fathaah, S.A.[S. Abdul], Raut, A.[Akash], Pournami, P.N., Jayaraj, P.B.,
Cervix type detection using a self-supervision boosted object detection technique,
IJIST(32), No. 5, 2022, pp. 1615-1630.
DOI Link 2209
cervical cancer, cervix, cervix type, classification, deep learning, object detector, self-supervision BibRef

Chen, T.T.[Ting-Ting], Zheng, W.H.[Wen-Hao], Ying, H.[Haochao], Tan, X.Y.[Xiang-Yu], Li, K.[Kexin], Li, X.P.[Xiao-Ping], Chen, D.Z.[Danny Z.], Wu, J.[Jian],
A Task Decomposing and Cell Comparing Method for Cervical Lesion Cell Detection,
MedImg(41), No. 9, September 2022, pp. 2432-2442.
IEEE DOI 2209
Lesions, Task analysis, Annotations, Feature extraction, Visualization, Cervical cancer, Image segmentation, cervical cytology images BibRef

Chitra, B., Kumar, S.S.,
Early cervical cancer diagnosis using Sooty tern-optimized CNN-LSTM classifier,
IJIST(32), No. 6, 2022, pp. 1846-1860.
DOI Link 2212
cervical cancer, convolutional neural network, KWFLICM model, LSTM, Sooty tern algorithm BibRef

Bingol, H.[Harun],
NCA-based hybrid convolutional neural network model for classification of cervical cancer on gauss-enhanced pap-smear images,
IJIST(32), No. 6, 2022, pp. 1978-1989.
DOI Link 2212
cervical cancer, deep learning, gauss method, NCA, pap-smear images BibRef

Kutty, S.K.[Sabeena Karim], Menon, G.C.[Gopakumar Chandrasekhara],
Enhancing convolutional neural network model with spectral features for the identification of cervical dysplasia,
IJIST(32), No. 6, 2022, pp. 1916-1927.
DOI Link 2212
CNN, feature selection, Haar transform, pap images, Random Forest BibRef

Xia, W.[Wenyao], Ameri, G.[Golafsoun], Fakim, D.[Djalal], Akhuanzada, H.[Humayon], Raza, M.Z.[Malik Z.], Shobeiri, S.A.[S. Abbas], McLean, L.[Linda], Chen, E.C.S.[Elvis C. S.],
Automatic Plane of Minimal Hiatal Dimensions Extraction From 3D Female Pelvic Floor Ultrasound,
MedImg(41), No. 12, December 2022, pp. 3873-3883.
IEEE DOI 2212
Ultrasonic imaging, Floors, Muscles, Image edge detection, Imaging, Phase frequency detectors, 3D transperineal ultrasound, pelvic organ prolapse BibRef

Jin, S.[Shan], Xu, H.M.[Hong-Ming], Dong, Y.[Yue], Hao, X.Y.[Xin-Yu], Qin, F.Y.[Feng-Ying], Xu, Q.[Qi], Zhu, Y.[Yong], Cong, F.Y.[Feng-Yu],
Automatic cervical cancer segmentation in multimodal magnetic resonance imaging using an EfficientNet encoder in UNet++ architecture,
IJIST(33), No. 1, 2023, pp. 362-377.
DOI Link 2301
cervical cancer, deep learning, magnetic resonance imaging, tumor segmentation BibRef

Sundari, M.J.[M. Jeya], Brintha, N.C.,
Factorization-based active contour segmentation and pelican optimization-based modified bidirectional long short-term memory for ovarian tumor detection,
IJIST(33), No. 1, 2023, pp. 230-245.
DOI Link 2301
3D CNN, MBiLSTM, multiclass classification, ovarian tumor, pelican optimization, pre-emphasis filter BibRef

Meng, Z.L.[Zhe-Ling], Zhu, Y.Y.[Yang-Yang], Pang, W.J.[Wen-Jing], Tian, J.[Jie], Nie, F.[Fang], Wang, K.[Kun],
MSMFN: An Ultrasound Based Multi-Step Modality Fusion Network for Identifying the Histologic Subtypes of Metastatic Cervical Lymphadenopathy,
MedImg(42), No. 4, April 2023, pp. 996-1008.
IEEE DOI 2304
Ultrasonic imaging, Task analysis, Lesions, Clinical diagnosis, Feature extraction, Neck, Lymph nodes, Deep learning, cervical lymphadenopathy BibRef

Wen, L.[Lu], Xiao, J.H.[Jiang-Hong], Zeng, J.[Jie], Zu, C.[Chen], Wu, X.[Xi], Zhou, J.[Jiliu], Peng, X.C.[Xing-Chen], Wang, Y.[Yan],
Multi-level progressive transfer learning for cervical cancer dose prediction,
PR(141), 2023, pp. 109606.
Elsevier DOI 2306
Radiation therapy, Dose prediction, Transfer learning, Deep neural network BibRef

He, Q.M.[Qi-Ming], Wang, C.J.[Cheng-Jiang], Zeng, S.Q.[Si-Qi], Liang, Z.D.[Zhen-Dong], Duan, H.[Hufei], Yang, J.Y.[Jing-Ying], Pan, F.[Feiyang], He, Y.H.[Yong-Hong], Huang, W.T.[Wen-Ting], Guan, T.[Tian],
Registration-enhanced multiple instance learning for cervical cancer whole slide image classification,
IJIST(34), No. 1, 2024, pp. e22952.
DOI Link 2401
cervical cancer grading, image registration, multiple instance learning, self-attention mechanisms, self-supervised learning BibRef


Lubrano, M.[Mélanie], Lazard, T.[Tristan], Balezo, G.[Guillaume], Bellahsen-Harrar, Y.[Yaëlle], Badoual, C.[Cécile], Berlemont, S.[Sylvain], Walter, T.[Thomas],
Automatic Grading of Cervical Biopsies by Combining Full and Self-supervision,
MIA-COVID19D22(408-423).
Springer DOI 2304
BibRef

Takizawa, H.[Hotaka], Fujinaka, A.[Ayano], Gunji, E.[Erika], Mekata, K.[Kojiro], Kudo, H.[Hiroyuki],
Preprocessing Optimization and Semantic Segmentation for Extraction of Cervical Intervertebral Disks from Videofluorography,
ICPR22(4948-4952)
IEEE DOI 2212
Atmospheric measurements, Semantic segmentation, Simulated annealing, Nonlinear filters, Filtering algorithms, Particle measurements BibRef

He, R.[Ruiwen], Benhabiles, H.[Halim], Windal, F.[Feryal], Even, G.[Gaël], Audebert, C.[Christophe], Collard, D.[Dominique], Taleb-Ahmed, A.[Abdelmalik],
A Cervix Detection Driven Deep Learning Approach for Cow Heat Analysis from Endoscopic Images,
ICIP22(3672-3676)
IEEE DOI 2211
Not people. Heating systems, Deep learning, Analytical models, Focusing, Cows, Predictive models, Deep learning, Transformer, Endoscopic images, Cervix detection BibRef

Mosiichuk, V.[Vladyslav], Viana, P.[Paula], Oliveira, T.[Tiago], Rosado, L.[Luís],
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ICIP21(101-105)
IEEE DOI 2201
Pathology, Image segmentation, Image recognition, Graphical models, Morphology, Feature extraction, Complexity theory, Histopathological Image Classification BibRef

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Meng, Z.[Zhu], Zhao, Z.C.[Zhi-Cheng], Su, F.[Fei], Guo, L.[Limei],
Hierarchical Spatial Pyramid Network For Cervical Precancerous Segmentation By Reconstructing Deep Segmentation Networks,
CVMI21(3733-3740)
IEEE DOI 2109
Pathology, Image segmentation, Fuses, Medical services, Feature extraction, Pattern recognition BibRef

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A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture,
ICPR21(9203-9210)
IEEE DOI 2105
Training, Image segmentation, Visualization, Navigation, Endoscopes, Surgery, Computer architecture, deep learning, ureteroscopy, lumen segmentation BibRef

Yaar, A., Asif, A., Raza, S.E.A.[S. E. Ahmed], Rajpoot, N., Minhas, F.,
Cross-Domain Knowledge Transfer for Prediction of Chemosensitivity in Ovarian Cancer Patients,
VL3W20(4020-4025)
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Wang, R., Kamata, S.,
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ICPR18(3941-3946)
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Plissiti, M.E., Dimitrakopoulos, P., Sfikas, G., Nikou, C., Krikoni, O., Charchanti, A.,
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ICIP18(3144-3148)
IEEE DOI 1809
Training, Databases, Feature extraction, Shape, Support vector machines, Computer architecture, Neural networks, convolutional neural network BibRef

Forslid, G., Wieslander, H., Bengtsson, E., Wählby, C., Hirsch, J.M., Stark, C.R., Sadanandan, S.K.,
Deep Convolutional Neural Networks for Detecting Cellular Changes Due to Malignancy,
BioIm17(82-89)
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Cavity resonators, Cervical cancer, Image analysis, Microscopy, Training, Tumors BibRef

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IPTA16(1-6)
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cellular biophysics BibRef

Saha, R., Bajger, M., Lee, G.,
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Unsupervised Segmentation of Cervical Cell Images Using Gaussian Mixture Model,
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Segmentation of Overlapping Cervical Cells in Microscopic Images with Superpixel Partitioning and Cell-Wise Contour Refinement,
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Skin Cancer, Melanoma .


Last update:Mar 16, 2024 at 20:36:19