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PRL(29), No. 9, 1 July 2008, pp. 1404-1415.
Elsevier DOI
0711
BibRef
Earlier:
Bimodal Texture Segmentation with the Lee-Seo Model,
ICIAR07(246-253).
Springer DOI
0708
BibRef
Earlier:
An LBP-Based Active Contour Algorithm for Unsupervised Texture
Segmentation,
ICPR06(II: 279-282).
IEEE DOI
0609
LBP: Local binary patterns; Texture segmentation; Active contours
BibRef
Mylona, E.A.,
Savelonas, M.A.,
Maroulis, D.E.,
Self-adjusted active contours using multi-directional texture cues,
ICIP13(3026-3030)
IEEE DOI
1402
Active Contours
BibRef
Keramidas, E.G.[Eystratios G.],
Iakovidis, D.K.[Dimitris K.],
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Karkanis, S.A.[Stavros A.],
Efficient and Effective Ultrasound Image Analysis Scheme for Thyroid
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Savelonas, M.A.[Michalis A.],
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An Active Contour Model Guided by LBP Distributions,
ACIVS06(197-207).
Springer DOI
0609
BibRef
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ICPR06(III: 976-979).
IEEE DOI
0609
BibRef
Savelonas, M.A.[Michalis A.],
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Iakovidis, D.K.[Dimitris K.],
Karkanis, S.A.[Stavros A.],
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A Variable Background Active Contour Model for Automatic Detection of
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0512
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From ambiguities to insights in cancer diagnosis via query-based
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Gene expression; Query; Thyroid cancer
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Support vector machines; Feature selection; Thyroid nodule classification
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1209
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Song, P.F.[Peng-Fei],
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1307
Acoustic radiation force
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Mellema, D.C.[Daniel C.],
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1609
Biological tissues
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Comb-Push Ultrasound Shear Elastography (CUSE) for Evaluation of
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1502
Young's modulus
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biomedical ultrasonics
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2002
computer-aided diagnosis (CAD) system,
deep convolutional neural network, deep learning, fine-tuning,
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Mugasa, H.[Hatwib],
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Koh, J.E.W.[Joel E.W.],
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2004
Feature extraction, Image filtering,
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Deep learning, Histopathology, Transfer learning,
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IEEE DOI
2106
Thyroid, Lesions, Cancer, Ultrasonic imaging, Pathology, Visualization,
Task analysis, Contrast-enhanced ultrasound, hierarchical,
thyroid nodule
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Hybrid classification with meta-heuristic-enabled optimal feature
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DOI Link
2108
C, NN, FU-SLnO model, image features, NN, PCA,
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Fagioli, A.[Alessio],
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Grani, G.[Giorgio],
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Multimodal Feature Fusion and Knowledge-Driven Learning via Experts
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CirSysVideo(32), No. 5, May 2022, pp. 2527-2534.
IEEE DOI
2205
Thyroid, Discrete wavelet transforms, Training, Task analysis,
Neural networks, Medical diagnostic imaging,
transfer learning
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Zhao, S.X.[Shi-Xuan],
Chen, Y.[Yang],
Yang, K.F.[Kai-Fu],
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A Local and Global Feature Disentangled Network: Toward
Classification of Benign-Malignant Thyroid Nodules From Ultrasound
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MedImg(41), No. 6, June 2022, pp. 1497-1509.
IEEE DOI
2206
Feature extraction, Thyroid, Cancer, Ultrasonic imaging,
Task analysis, Deep learning, Radiomics, Ultrasound image,
deep neural network
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Tan, G.H.[Guang-Hua],
Luo, H.X.[Hong-Xia],
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Li, S.L.[Sheng-Li],
Li, K.[Kenli],
A Novel Deep Learning Framework for Automatic Recognition of Thyroid
Gland and Tissues of Neck in Ultrasound Image,
CirSysVideo(32), No. 9, September 2022, pp. 6113-6124.
IEEE DOI
2209
Thyroid, Ultrasonic imaging, Image segmentation, Neck, Deep learning,
Task analysis, Medical diagnostic imaging, Automatic recognition,
thyroid gland
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Fu, C.[Chao],
Hou, B.B.[Bing-Bing],
Xue, M.[Min],
Chang, L.L.[Lei-Lei],
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Extended Belief Rule-Based System With Accurate Rule Weights and
Efficient Rule Activation for Diagnosis of Thyroid Nodules,
SMCS(53), No. 1, January 2023, pp. 251-263.
IEEE DOI
2301
Weight measurement, Thyroid, Optimization, Inference algorithms,
Size measurement, Hospitals, Explosions, Accurate rule weight,
extended belief-rule-based (EBRB) system
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Li, Z.Z.[Zhi-Zhou],
Zhou, S.[Shichong],
Chang, C.[Cai],
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Guo, Y.[Yi],
A weakly supervised deep active contour model for nodule segmentation
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PRL(165), 2023, pp. 128-137.
Elsevier DOI
2301
Ultrasound images, Thyroid nodule segmentation, Weakly supervised segmentation,
Contour deformation network, Edge attention module
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Ongole, D.[Devanand],
Saravanan, S.,
Colour-based segmentation using FCM and K-means clustering for 3D
thyroid gland state image classification using deep convolutional
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IJIST(33), No. 5, 2023, pp. 1814-1826.
DOI Link
2310
colour based FCM and K-mean clustering, Deep-CNN model, GLCM,
TI-RADS classification
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Liu, Q.[Qiong],
Li, Y.[Yue],
Zhai, Z.X.[Zi-Xin],
Jia, H.Y.[Hai-Yan],
Liu, L.P.[Li-Ping],
An improved method for thyroid nodule ultrasound image segmentation
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IJIST(33), No. 6, 2023, pp. 2118-2127.
DOI Link
2311
computer-aided diagnosis, image segmentation, thyroid nodule,
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Luo, X.[Xiang],
Li, Z.Y.[Zhong-Yu],
Xu, C.[Canhua],
Zhang, B.[Bite],
Zhang, L.L.[Liang-Liang],
Zhu, J.[Jihua],
Huang, P.[Peng],
Wang, X.[Xin],
Yang, M.[Meng],
Chang, S.[Shi],
Semi-Supervised Thyroid Nodule Detection in Ultrasound Videos,
MedImg(43), No. 5, May 2024, pp. 1792-1803.
IEEE DOI
2405
Ultrasonic imaging, Thyroid, Videos, Annotations, Training,
Feature extraction, Solid modeling, Video object detection, pseudo labels
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Tajbakhsh, K.[Kiarash],
Stanowska, O.[Olga],
Neels, A.[Antonia],
Perren, A.[Aurel],
Zboray, R.[Robert],
3D Virtual Histopathology by Phase-Contrast X-Ray Micro-CT for
Follicular Thyroid Neoplasms,
MedImg(43), No. 7, July 2024, pp. 2670-2678.
IEEE DOI
2407
Histopathology, X-ray imaging, Thyroid, Imaging, Neoplasms,
Electron tubes, Thyroid neoplasm, 3D virtual histology,
precision medicine
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Chen, F.[Fang],
Han, H.J.[Hao-Jie],
Wan, P.[Peng],
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Wen, B.[Baojie],
Liu, C.[Chunrui],
Zhang, D.Q.[Dao-Qiang],
Do as Sonographers Think: Contrast-Enhanced Ultrasound for Thyroid
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MedImg(43), No. 11, November 2024, pp. 3881-3894.
IEEE DOI
2411
Thyroid, Gray-scale, Lesions, Ultrasonic imaging, Cancer, Videos,
Feature extraction, Contrast-enhanced ultrasound, infiltrative expansion
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Zhao, X.Y.[Xing-Yue],
Li, Z.Y.[Zhong-Yu],
Luo, X.D.[Xiang-De],
Li, P.Q.[Pei-Qi],
Huang, P.[Peng],
Zhu, J.W.[Jian-Wei],
Liu, Y.[Yang],
Zhu, J.[Jihua],
Yang, M.[Meng],
Chang, S.[Shi],
Dong, J.[Jun],
Ultrasound Nodule Segmentation Using Asymmetric Learning With Simple
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CirSysVideo(34), No. 10, October 2024, pp. 9010-9023.
IEEE DOI
2411
Annotations, Image segmentation, Training, Shape, Ultrasonic imaging,
Lesions, Thyroid, Ultrasound nodule segmentation,
aspect ratio annotations
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Shalini, L.,
Vijayakumar, K.,
An Efficient JSH-FCM-Based Thyroid Disease Detection Using Ash-Ann with
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IPTA22(1-6)
IEEE DOI
2206
Pathology, Computational modeling, Computer architecture,
Feature extraction, Transformers, Convolutional neural networks,
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Qiu, S.[Shuhao],
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Attention Based Multi-Instance Thyroid Cytopathological Diagnosis
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ICPR21(3536-3541)
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2105
Deep learning, Visualization, Histopathology, Supervised learning,
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Li, S.,
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Qin, H.,
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Learning from Weakly-Labeled Clinical Data for Automatic Thyroid
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ICIP18(3114-3118)
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1809
Cancer, Proposals, Image edge detection, Training,
Biomedical imaging, Feature extraction, Ultrasonic imaging,
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Bao, G.,
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3D Segmentation of Residual Thyroid Tissue Using Constrained Region
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DICTA17(1-5)
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1804
biological tissues, cancer, computerised tomography,
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Lymph Nodes .