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Medical image analysis; Image segmentation; Graph cuts; Cervical
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Pap smear test; Cell grading; Automatic thresholding; Hierarchical
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biomedical optical imaging
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1601
Cancer
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Cervical cancer screening
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1701
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1906
Cervical cancer
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1702
Image segmentation
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Ultrasonic imaging,
Wavelet transforms, Manuals,
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1812
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1902
Shape, Planning, Libraries, Bladder, Strain, Deformable models,
Computed tomography, Radiotherapy, cervical,
planning library
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Probabilistic model, Image quality assessment, Image recognition
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1912
Shape, Image segmentation, Task analysis, Feature extraction,
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automatic cervical cancer screening
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2005
cancer, cervical, classifications, fuzzy logic, segmentation
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Chen, J.,
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Ma, K.,
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Computer-Aided Cervical Cancer Diagnosis Using Time-Lapsed
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IEEE DOI
2011
Machine learning, Feature extraction, Cervical cancer, Lesions,
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feature fusion
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multithresholding, nucleus and cytoplasm, pap smear, SVM classifiers
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2102
atlas-based segmentation, cervical cancer,
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2104
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Hao, D.M.[Dong-Mei],
Song, X.X.[Xiao-Xiao],
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Yang, L.[Lin],
Liu, X.H.[Xiao-Hong],
Jiang, H.Q.[Hong-Qing],
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Effect of electrode configuration on recognizing uterine contraction
with electrohysterogram: Analysis using a convolutional neural
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IJIST(31), No. 2, 2021, pp. 972-980.
DOI Link
2105
convolutional neural network, electrode configuration,
electrohysterogram, uterine contraction
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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
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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
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Dong, Y.[Yang],
Wan, J.C.[Jia-Chen],
Wang, X.J.[Xing-Jian],
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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
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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
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Su, L.[Limei],
Huang, S.J.[Shen-Jiao],
Wang, Z.Y.[Zheng-Yin],
Zhang, Z.Q.[Zhi-Qin],
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Chen, T.S.[Tong-Sheng],
Whole slide cervical image classification based on convolutional
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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
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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
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PR(130), 2022, pp. 108829.
Elsevier DOI
2206
Convolutional neural network, Visual transformer,
Multilayer perceptron, Cervical cell classification, Image classification
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Mahyari, T.L.[Tayebeh Lotfi],
Dansereau, R.M.[Richard M.],
Multi-layer random walker image segmentation for overlapped cervical
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IET-IPR(16), No. 11, 2022, pp. 2959-2972.
DOI Link
2208
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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
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IJIST(32), No. 5, 2022, pp. 1615-1630.
DOI Link
2209
cervical cancer, cervix, cervix type, classification,
deep learning, object detector, self-supervision
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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
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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
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Chitra, B.,
Kumar, S.S.,
Early cervical cancer diagnosis using Sooty tern-optimized CNN-LSTM
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IJIST(32), No. 6, 2022, pp. 1846-1860.
DOI Link
2212
cervical cancer, convolutional neural network, KWFLICM model,
LSTM, Sooty tern algorithm
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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
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Kutty, S.K.[Sabeena Karim],
Menon, G.C.[Gopakumar Chandrasekhara],
Enhancing convolutional neural network model with spectral features
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IJIST(32), No. 6, 2022, pp. 1916-1927.
DOI Link
2212
CNN, feature selection, Haar transform, pap images, Random Forest
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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
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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
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IJIST(33), No. 1, 2023, pp. 362-377.
DOI Link
2301
cervical cancer, deep learning, magnetic resonance imaging, tumor segmentation
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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
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Zhu, Y.Y.[Yang-Yang],
Pang, W.J.[Wen-Jing],
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MSMFN: An Ultrasound Based Multi-Step Modality Fusion Network for
Identifying the Histologic Subtypes of Metastatic Cervical
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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,
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Elsevier DOI
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IJIST(34), No. 1, 2024, pp. e22952.
DOI Link
2401
cervical cancer grading, image registration,
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Cervical-YOSA: Utilizing prompt engineering and pre-trained
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2411
biomedical MRI, image segmentation, transforms
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ICPR22(4948-4952)
IEEE DOI
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Atmospheric measurements, Semantic segmentation,
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A Cervix Detection Driven Deep Learning Approach for Cow Heat
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ICIP22(3672-3676)
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2205
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Nuclear Density Distribution Feature for Improving Cervical
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ICIP21(101-105)
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2201
Pathology, Image segmentation, Image recognition, Graphical models,
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2112
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2112
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CVMI21(3733-3740)
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A Lumen Segmentation Method in Ureteroscopy Images based on a Deep
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Training, Image segmentation, Visualization, Navigation, Endoscopes,
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VL3W20(4020-4025)
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Sipakmed: A New Dataset for Feature and Image Based Classification of
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ICIP18(3144-3148)
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Training, Databases, Feature extraction, Shape,
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Deep Convolutional Neural Networks for Detecting Cellular Changes Due
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BioIm17(82-89)
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cellular biophysics
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Cervical cancer
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Skin Cancer, Melanoma .