21.14.3.1 Medical Applications -- Colonoscopy, Polyp Detection, Analysis

Gokturk, S.B., Tomasi, C., Acar, B., Beaulieu, C.F., Paik, D.S., Jeffrey, R.B., Yee, J., Napel, S.,
A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography,
MedImg(20), No. 12, December 2001, pp. 1251-1260.
IEEE Top Reference. 0201
BibRef

Acar, B., Beaulieu, C.F., Gokturk, S.B., Tomasi, C., Paik, D.S., Jeffrey, R.B., Yee, J., Napel, S.,
Edge displacement field-based classification for improved detection of polyps in CT colonography,
MedImg(21), No. 12, December 2002, pp. 1461-1467.
IEEE Top Reference. 0301
BibRef

Yoshida, H., Nappi, J.,
Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps,
MedImg(20), No. 12, December 2001, pp. 1261-1274.
IEEE Top Reference. 0201
BibRef

Yao, J.H., Miller, M., Franaszek, M., Summers, R.M.,
Colonic Polyp Segmentation in CT Colonography-Based on Fuzzy Clustering and Deformable Models,
MedImg(23), No. 11, November 2004, pp. 1344-1352.
IEEE Abstract. 0411
BibRef

Konukoglu, E., Acar, B., Paik, D.S., Beaulieu, C.F., Rosenberg, J., Napel, S.,
Polyp Enhancing Level Set Evolution of Colon Wall: Method and Pilot Study,
MedImg(26), No. 12, December 2007, pp. 1649-1656.
IEEE DOI 0712
BibRef

Hafner, M.[Michael], Kwitt, R.[Roland], Uhl, A.[Andreas], Wrba, F.[Friedrich], Gangl, A.[Alfred], Vecsei, A.[Andreas],
Computer-assisted pit-pattern classification in different wavelet domains for supporting dignity assessment of colonic polyps,
PR(42), No. 6, June 2009, pp. 1180-1191.
Elsevier DOI 0902
Computer-assisted pit-pattern classification; Wavelet transformation; Colorectal cancer; Color-texture analysis BibRef

Kwitt, R.[Roland], Uhl, A.[Andreas], Hafner, M.[Michael], Gangl, A.[Alfred], Wrba, F.[Friedrich], Vecsei, A.[Andreas],
Predicting the histology of colorectal lesions in a probabilistic framework,
MMBIA10(103-110).
IEEE DOI 1006
BibRef

Häfner, M., Gangl, A., Liedlgruber, M., Uhl, A.[Andreas], Vécsei, A., Wrba, F.,
Endoscopic Image Classification Using Edge-Based Features,
ICPR10(2724-2727).
IEEE DOI 1008
BibRef
And:
Classification of Endoscopic Images Using Delaunay Triangulation-Based Edge Features,
ICIAR10(II: 131-140).
Springer DOI 1006
BibRef

Uhl, A.[Andreas], Vécsei, A.[Andreas], Wimmer, G.[Georg],
Complex Wavelet Transform Variants in a Scale Invariant Classification of Celiac Disease,
IbPRIA11(742-749).
Springer DOI 1106
BibRef

Yao, J.H.[Jian-Hua], Li, J.[Jiang], Summers, R.M.[Ronald M.],
Employing topographical height map in colonic polyp measurement and false positive reduction,
PR(42), No. 6, June 2009, pp. 1029-1040.
Elsevier DOI 0902
CAD; Colonic polyps; Topographical height map; Range image BibRef

van Ravesteijn, V.F., van Wijk, C., Vos, F.M., Truyen, R., Peters, J.F., Stoker, J., van Vliet, L.J.,
Computer-Aided Detection of Polyps in CT Colonography Using Logistic Regression,
MedImg(29), No. 1, January 2010, pp. 120-131.
IEEE DOI 1001
BibRef

van Wijk, C., van Ravesteijn, V.F.[Vincent F.], Vos, F.M.[Frans M.], van Vliet, L.J.[Lucas J.],
Detection and Segmentation of Colonic Polyps on Implicit Isosurfaces by Second Principal Curvature Flow,
MedImg(29), No. 3, March 2010, pp. 688-698.
IEEE DOI 1003
BibRef

van Ravesteijn, V.F.[Vincent F.], Vos, F.M.[Frans M.], van Vliet, L.J.[Lucas J.],
Recognition of Protruding Objects in Highly Structured Surroundings by Structural Inference,
SCIA09(41-50).
Springer DOI 0906
BibRef

van Ravesteijn, V.F., Vos, F.M., Serlie, I.W.O., Truyen, R., van Vliet, L.J.,
Thin layer tissue classification for electronic cleansing of CT colonography data,
ICPR08(1-5).
IEEE DOI 0812
BibRef

Chowdhury, A.S.[Ananda S.], Tan, S.[Sovira], Yao, J.H.[Jian-Hua], Summers, R.M.[Ronald M.],
Colonic fold detection from computed tomographic colonography images using diffusion-FCM and level sets,
PRL(31), No. 9, 1 July 2010, pp. 876-883.
Elsevier DOI 1004
Heat diffusion; Fuzzy c-means; Level sets; Shape index; Computed tomographic colonography BibRef

Wei, Z.S.[Zhuo-Shi], Yao, J.H.[Jian-Hua], Wang, S.J.[Shi-Jun], Summers, R.M.[Ronald M.],
Teniae Coli Extraction in Human Colon for Computed Tomographic Colonography Images,
VirtualColon10(98-104).
Springer DOI 1112
BibRef

Chowdhury, A.S., Yao, J.H.[Jian-Hua], van Uitert, R.L., Linguraru, M.G., Summers, R.M.[Ronald M.],
Detection of anatomical landmarks in human colon from computed tomographic colonography images,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Lamy, J.[Julien], Summers, R.M.[Ronald M.],
Teniæ Coli Detection from Colon Surface: Extraction of Anatomical Markers for Virtual Colonoscopy,
ISVC07(I: 199-207).
Springer DOI 0711
BibRef

Yao, J.H.[Jian-Hua], Li, J.[Jiang], Summers, R.M.[Ronald M.],
CT Colonography Computer-Aided Polyp Detection using Topographical Height Map,
ICIP07(V: 21-24).
IEEE DOI 0709
BibRef

Wang, S.J.[Shi-Jun], Yao, J.H.[Jian-Hua], Summers, R.M.[Ronald M.],
Matching colonic polyps from prone and supine CT colonography scans based on statistical curvature information,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Huang, A.[Adam], Li, J.[Jiang], Summers, R.M.[Ronald M.], Petrick, N.[Nicholas], Hara, A.K.[Amy K.],
Improving polyp detection algorithms for CT colonography: Pareto front approach,
PRL(31), No. 11, 1 August 2010, pp. 1461-1469.
Elsevier DOI 1008
Pareto front; Computer-aided detection; Polyp detection; CT colonography; Virtual colonoscopy BibRef

Suzuki, K.[Kenji], Zhang, J., Xu, J.W.[Jian-Wu],
Massive-Training Artificial Neural Network Coupled With Laplacian-Eigenfunction-Based Dimensionality Reduction for Computer-Aided Detection of Polyps in CT Colonography,
MedImg(29), No. 11, November 2010, pp. 1907-1917.
IEEE DOI 1011
BibRef

Xu, J.W.[Jian-Wu], Suzuki, K.[Kenji],
Computer-Aided Detection of Polyps in CT Colonography with Pixel-Based Machine Learning Techniques,
MLMI11(360-367).
Springer DOI 1109
BibRef
Earlier:
False-Positive Reduction in Computer-Aided Detection of Polyps in CT Colonography: A Massive-Training Support Vector Regression Approach,
VirtualColon10(47-52).
Springer DOI 1112
BibRef

Suzuki, K.[Kenji],
Recent Advances in Reduction of False Positives in Computerized Detection of Polyps in CT Colonography,
VirtualColon10(32-39).
Springer DOI 1112
BibRef

Ong, J.L.[Ju Lynn], Seghouane, A.K.[Abd-Krim],
Feature selection using mutual information in CT colonography,
PRL(32), No. 2, 15 January 2011, pp. 337-341.
Elsevier DOI 1101
BibRef
Earlier: A2, A1:
Efficient feature selection for polyp detection,
ICIP10(2285-2288).
IEEE DOI 1009
Feature selection; Computed tomography; Support vector classifier; Mutual information BibRef

Ong, J.L.[Ju Lynn], Seghouane, A.K.[Abd-Krim],
From Point to Local Neighborhood: Polyp Detection in CT Colonography Using Geodesic Ring Neighborhoods,
IP(20), No. 4, April 2011, pp. 1000-1010.
IEEE DOI 1103
BibRef
Earlier: A2, A1:
Geodesic-ring based curvature maps for polyp detection in CT colonography,
ICIP10(1421-1424).
IEEE DOI 1009
BibRef

Ong, J.L.[Ju Lynn], Seghouane, A.K., Osborn, K.,
Mean Shape Models for Polyp Detection in CT Colonography,
DICTA08(287-293).
IEEE DOI 0812
BibRef

Bernal, J., Sánchez, J., Vilariño, F.,
Towards automatic polyp detection with a polyp appearance model,
PR(45), No. 9, September 2012, pp. 3166-3182.
Elsevier DOI 1206
Colonoscopy; Polyp detection; Region segmentation; SA-DOVA descriptor BibRef

Vazquez, E.[Eduard], Yang, X.Y.[Xiao-Yun], Slabaugh, G.G.[Greg G.],
Erosion band signatures for spatial extraction of features,
MVA(24), No. 4, May 2013, pp. 695-705.
Springer DOI 1304
spatial coherence of features extracted from a region. Apply to polyp detection and head tracking. BibRef

Yang, X.Y.[Xiao-Yun], Beddoe, G.[Gareth], Slabaugh, G.G.[Greg G.],
Learning to Detect 3D Rectal Tubes in CT Colonography Using a Global Shape Model,
VirtualColon10(53-59).
Springer DOI 1112
BibRef

Boyes, R.[Richard], Slabaugh, G.G.[Greg G.], Beddoe, G.[Gareth],
Fast pseudo-enhancement correction in CT colonography using linear shift-invariant filters,
ICIP09(2509-2512).
IEEE DOI 0911
BibRef

Ye, X.J.[Xu-Jiong], Beddoe, G.[Gareth], Slabaugh, G.G.[Greg G.],
A Bayesian Approach for False Positive Reduction in CTC CAD,
VirtualColon10(40-46).
Springer DOI 1112
BibRef

Boyes, R.[Richard], Ye, X.J.[Xu-Jiong], Beddoe, G.[Gareth], Slabaugh, G.G.[Greg G.],
Sparse Parallel Electronic Bowel Cleansing in CT Colonography,
3DPVT10(xx-yy).
WWW Link. 1005
BibRef

Mamonov, A.V., Figueiredo, I.N., Figueiredo, P.N., Tsai, Y.H.R.[Y.H. Richard],
Automated Polyp Detection in Colon Capsule Endoscopy,
MedImg(33), No. 7, July 2014, pp. 1488-1502.
IEEE DOI 1407
Biological tissues BibRef

Bernal, J.[Jorge],
Polyp Localization and Segmentation in Colonoscopy Images by Means of a Model of Appearance for Polyps,
ELCVIA(13), No. 2, 2014, pp. xx-yy.
DOI Link 1407
Ph.D.. Thesis. BibRef

Bernal, J.[Jorge], Sánchez, J.[Javier], Vilariño, F.[Fernando],
A Region Segmentation Method for Colonoscopy Images Using a Model of Polyp Appearance,
IbPRIA11(134-142).
Springer DOI 1106
BibRef

Bae, S.H.[Seung-Hwan], Yoon, K.J.[Kuk-Jin],
Polyp Detection via Imbalanced Learning and Discriminative Feature Learning,
MedImg(34), No. 11, November 2015, pp. 2379-2393.
IEEE DOI 1511
Detectors BibRef

Tajbakhsh, N., Gurudu, S.R., Liang, J.,
Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information,
MedImg(35), No. 2, February 2016, pp. 630-644.
IEEE DOI 1602
Cancer BibRef

Roth, H.R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., Summers, R.M.,
Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation,
MedImg(35), No. 5, May 2016, pp. 1170-1181.
IEEE DOI 1605
Colonic polyps BibRef

Hu, Y., Liang, Z., Song, B., Han, H., Pickhardt, P.J., Zhu, W., Duan, C., Zhang, H., Barish, M.A., Lascarides, C.E.,
Texture Feature Extraction and Analysis for Polyp Differentiation via Computed Tomography Colonography,
MedImg(35), No. 6, June 2016, pp. 1522-1531.
IEEE DOI 1606
Cancer BibRef

Bernal, J., Tajkbaksh, N., Sánchez, F.J., Matuszewski, B.J., Chen, H., Yu, L., Angermann, Q., Romain, O., Rustad, B., Balasingham, I., Pogorelov, K., Choi, S., Debard, Q., Maier-Hein, L., Speidel, S., Stoyanov, D., Brandao, P., Córdova, H., Sánchez-Montes, C., Gurudu, S.R., Fernández-Esparrach, G., Dray, X., Liang, J., Histace, A.,
Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge,
MedImg(36), No. 6, June 2017, pp. 1231-1249.
IEEE DOI 1706
Biomedical imaging, Cancer, Colon, Colonoscopy, Databases, Endoscopes, Lesions, Endoscopic vision, handcrafted features, machine learning, polyp detection, validation, framework BibRef

Zhang, R.K.[Rui-Kai], Zheng, Y.[Yali], Poon, C.C.Y.[Carmen C.Y.], Shen, D.G.[Ding-Gang], Lau, J.Y.W.[James Y.W.],
Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker,
PR(83), 2018, pp. 209-219.
Elsevier DOI 1808
Smart cancer screening, Therapeutic endoscopy, Endoscopic Informatics, Body Sensor Network, Deep Learning, Health Informatics BibRef

Jia, X., Xing, X., Yuan, Y., Xing, L., Meng, M.Q.,
Wireless Capsule Endoscopy: A New Tool for Cancer Screening in the Colon With Deep-Learning-Based Polyp Recognition,
PIEEE(108), No. 1, January 2020, pp. 178-197.
IEEE DOI 2001
Cancer, Endoscopes, Deep learning, Machine learning, Medical treatment, Biomedical imaging, Cancer screening, wireless capsule endoscopy (WCE) BibRef

Mostafiz, R.[Rafid], Hasan, M.[Mosaddik], Hossain, I.[Imran], Rahman, M.M.[Mohammad M.],
An intelligent system for gastrointestinal polyp detection in endoscopic video using fusion of bidimensional empirical mode decomposition and convolutional neural network features,
IJIST(30), No. 1, 2020, pp. 224-233.
DOI Link 2002
CEMD features, convolutional neural network (CNN), empirical mode decomposition, support vector machine (SVM), video endoscopy BibRef

Tan, J., Gao, Y., Liang, Z., Cao, W., Pomeroy, M.J., Huo, Y., Li, L., Barish, M.A., Abbasi, A.F., Pickhardt, P.J.,
3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography,
MedImg(39), No. 6, June 2020, pp. 2013-2024.
IEEE DOI 2006
Polyp differentiation, image features, deep learning, GLCM, CT colonoscopy BibRef

Haj-Manouchehri, A.[Azadeh], Mohammadi, H.M.[Hossein Mahvash],
Polyp detection using CNNs in colonoscopy video,
IET-CV(14), No. 5, August 2020, pp. 241-247.
DOI Link 2007
BibRef

Kaçmaz, R.N.[Rukiye Nur], Yilmaz, B.[Bülent], Aydin, Z.[Zafer],
Effect of interpolation on specular reflections in texture-based automatic colonic polyp detection,
IJIST(31), No. 1, 2021, pp. 327-335.
DOI Link 2102
classification, colon polyp, image processing, machine learning, specular reflection BibRef

Guo, X., Yang, C., Liu, Y., Yuan, Y.,
Learn to Threshold: ThresholdNet With Confidence-Guided Manifold Mixup for Polyp Segmentation,
MedImg(40), No. 4, April 2021, pp. 1134-1146.
IEEE DOI 2104
Image segmentation, Training, Cancer, Feature extraction, Manifolds, Deep learning, Task analysis, Polyp segmentation, TMSG module BibRef

Afify, H.M.[Heba M.], Mohammed, K.K.[Kamel K.], Hassanien, A.E.[Aboul Ella],
An improved framework for polyp image segmentation based on SegNet architecture,
IJIST(31), No. 3, 2021, pp. 1741-1751.
DOI Link 2108
convolutional neural network, Kvasir-SEG database, polyp image segmentation, SegNet, VGG-16 network, VGG-19 network BibRef

Ghosh, S.K.[Swarup Kr], Ghosh, A.[Anupam],
A novel intuitionistic fuzzy soft set based colonogram enhancement for polyps localization,
IJIST(31), No. 3, 2021, pp. 1486-1502.
DOI Link 2108
colonography, contrast enhancement, fuzzy soft set, intuitionistic fuzzy set, polyps localization BibRef

Yildirim, M.[Muhammed], Cinar, A.[Ahmet],
Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA:NET,
IJIST(32), No. 1, 2022, pp. 155-162.
DOI Link 2201
classification, CNN, colon cancer, deep learning, image processing BibRef

Sasmal, P.[Pradipta], Bhuyan, M.K., Dutta, S.[Soumayan], Iwahori, Y.J.[Yu-Ji],
An unsupervised approach of colonic polyp segmentation using adaptive markov random fields,
PRL(154), 2022, pp. 7-15.
Elsevier DOI 2202
Endoscopic image, Super-pixel, Markov random field (MRF), Local binary pattern (LBP) BibRef

Liu, X.Y.[Xin-Yu], Yuan, Y.X.[Yi-Xuan],
A Source-Free Domain Adaptive Polyp Detection Framework With Style Diversification Flow,
MedImg(41), No. 7, July 2022, pp. 1897-1908.
IEEE DOI 2207
Adaptation models, Detectors, Data models, Task analysis, Training, Cancer, Object detection, Automatic polyp detection, source-free domain adaptation BibRef

Lin, Y.[Yi], Wu, J.[Jichun], Xiao, G.[Guobao], Guo, J.W.[Jun-Wen], Chen, G.[Geng], Ma, J.Y.[Jia-Yi],
BSCA-Net: Bit Slicing Context Attention network for polyp segmentation,
PR(132), 2022, pp. 108917.
Elsevier DOI 2209
Medical image segmentation, Polyp segmentation, Colonoscopy, Attention mechanism BibRef

Shi, J.H.[Jing-Hui], Zhang, Q.[Qing], Tang, Y.H.[Yu-Hao], Zhang, Z.Q.[Zhong-Qun],
Polyp-Mixer: An Efficient Context-Aware MLP-Based Paradigm for Polyp Segmentation,
CirSysVideo(33), No. 1, January 2023, pp. 30-42.
IEEE DOI 2301
Decoding, Transformers, Task analysis, Image segmentation, Feature extraction, Convolution, Computational modeling, MLP, polyp segmentation BibRef

Bhattacharya, D.[Debayan], Eggert, D.[Dennis], Betz, C.[Christian], Schlaefer, A.[Alexander],
Squeeze and multi-context attention for polyp segmentation,
IJIST(33), No. 1, 2023, pp. 123-142.
DOI Link 2301
attention, attention gate, polyp segmentation, squeeze and excite, squeeze and multi-context, U-Net BibRef

Sushama, G.[Geetha], Menon, G.C.[Gopakumar Chandrasekhara],
Attention augmented residual autoencoder for efficient polyp segmentation,
IJIST(33), No. 2, 2023, pp. 701-713.
DOI Link 2303
attention module, autoencoder, colon polyps, residual skip-connected CNN, semantic segmentation BibRef

Sharma, P.[Pallabi], Das, D.[Dipankar], Gautam, A.[Anmol], Balabantaray, B.K.[Bunil Kumar],
LPNet: A lightweight CNN with discrete wavelet pooling strategies for colon polyps classification,
IJIST(33), No. 2, 2023, pp. 495-510.
DOI Link 2303
deep learning, classification, CNN, polyps, colonoscopy BibRef

Wu, H.[Huisi], Zhao, Z.B.[Ze-Bin], Zhong, J.F.[Jia-Fu], Wang, W.[Wei], Wen, Z.K.[Zhen-Kun], Qin, J.[Jing],
PolypSeg+: A Lightweight Context-Aware Network for Real-Time Polyp Segmentation,
Cyber(53), No. 4, April 2023, pp. 2610-2621.
IEEE DOI 2303
Feature extraction, Image segmentation, Cancer, Real-time systems, Colonoscopy, Task analysis, Data mining, Colonoscopy, real-time polyp segmentation BibRef

Zhou, T.[Tao], Zhou, Y.[Yi], He, K.[Kelei], Gong, C.[Chen], Yang, J.[Jian], Fu, H.Z.[Hua-Zhu], Shen, D.G.[Ding-Gang],
Cross-level Feature Aggregation Network for Polyp Segmentation,
PR(140), 2023, pp. 109555.
Elsevier DOI 2305
Polyp segmentation, boundary-aware features, cross-level feature fusion, boundary aggregated module BibRef

Mushtaq, D.[Dania], Madni, T.M.[Tahir Mustafa], Janjua, U.I.[Uzair Iqbal], Anwar, F.[Fozia], Kakakhail, A.[Ahmad],
An automatic gastric polyp detection technique using deep learning,
IJIST(33), No. 3, 2023, pp. 866-880.
DOI Link 2305
attention mechanism, convolution neural network, feature map concatenation, gastric polyp, gastroscopy, SSD BibRef

Guo, Q.Q.[Qing-Qing], Fang, X.Y.[Xian-Yong], Wang, K.B.[Kai-Bing], Shi, Y.Q.[Yu-Qing], Wang, L.[Linbo], Zhang, E.[Enming], Liu, Z.Y.[Zheng-Yi],
Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules,
IET-IPR(17), No. 8, 2023, pp. 2503-2515.
DOI Link 2306
biomedical imaging, image segmentation BibRef

Al Jowair, H.[Hamdan], Alsulaiman, M.[Mansour], Muhammad, G.[Ghulam],
Multi parallel U-net encoder network for effective polyp image segmentation,
IVC(137), 2023, pp. 104767.
Elsevier DOI 2309
Polyp, Medical image segmentation, Deep learning, Multi encoders, Skip connections BibRef

Lu, L.[Lu], Chen, S.H.[Shu-Han], Tang, H.[Haonan], Zhang, X.F.[Xin-Feng], Hu, X.L.[Xue-Long],
A multi-scale perceptual polyp segmentation network based on boundary guidance,
IVC(138), 2023, pp. 104811.
Elsevier DOI 2310
Polyp segmentation, Boundary guidance, Multi-scale global perception, Complementary fusion, Detail refinement BibRef

Jiang, S.[Shen], Li, J.J.[Jin-Jiang], Hua, Z.[Zhen],
GR-Net: Gated axial attention ResNest network for polyp segmentation,
IJIST(33), No. 5, 2023, pp. 1531-1548.
DOI Link 2310
attention, colonoscopy, medical image segmentation, polyp segmentation BibRef

Chen, L.F.[Li-Fang], Ge, H.Z.[Hong-Ze], Li, J.W.[Jia-Wei],
CrossFormer: Multi-scale cross-attention for polyp segmentation,
IET-IPR(17), No. 12, 2023, pp. 3441-3452.
DOI Link 2310
channel enhancement, colorectal cancer, cross-attention, multi scale, polyp segmentation BibRef

Wang, Z.[Zhi], Gao, F.[Feng], Yu, L.[Long], Tian, S.W.[Sheng-Wei],
UACENet: Uncertain area attention and cross-image context extraction network for polyp segmentation,
IJIST(33), No. 6, 2023, pp. 1973-1987.
DOI Link 2311
attention mechanism, context feature learning, deep learning, polyp segmentation BibRef

Jain, S.[Samir], Atale, R.[Rohan], Gupta, A.[Anubhav], Mishra, U.[Utkarsh], Seal, A.[Ayan], Ojha, A.[Aparajita], Jaworek-Korjakowska, J.[Joanna], Krejcar, O.[Ondrej],
CoInNet: A Convolution-Involution Network With a Novel Statistical Attention for Automatic Polyp Segmentation,
MedImg(42), No. 12, December 2023, pp. 3987-4000.
IEEE DOI 2312
BibRef

Gao, S.[Shanglei], Zhan, Y.W.[Yin-Wei], Chen, Z.J.[Zi-Jun],
BGNet: Boundary-guided network for polyp segmentation,
IJIST(34), No. 1, 2024, pp. e22959.
DOI Link 2401
boundary-guided, colorectal cancer (CRC), encoder-decoder, polyp image segmentation BibRef

Li, W.X.[Wen-Xue], Xiong, X.Y.[Xin-Yu], Li, S.Y.[Si-Ying], Fan, F.[Fugui],
HybridVPS: Hybrid-Supervised Video Polyp Segmentation Under Low-Cost Labels,
SPLetters(31), 2024, pp. 111-115.
IEEE DOI 2401
BibRef

Zhao, Y.Y.[Yi-Yang], Li, J.J.[Jin-Jiang], Hua, Z.[Zhen],
TACT: Text attention based CNN-Transformer network for polyp segmentation,
IJIST(34), No. 2, 2024, pp. e22997.
DOI Link 2402
CNN-Transformer, colonoscopy, medical image segmentation, polyp segmentation BibRef

Selvaraj, J.[Jothiraj], Jayanthy, A.K.,
Design and development of artificial intelligence-based application programming interface for early detection and diagnosis of colorectal cancer from wireless capsule endoscopy images,
IJIST(34), No. 2, 2024, pp. e23034.
DOI Link 2402
artificial intelligence, colorectal cancer, colorectal polyp, computer-aided diagnosis, deep learning, early diagnosis, wireless capsule endoscopy BibRef

Yue, G.H.[Guang-Hui], Li, Y.[Yuanyan], Jiang, W.C.[Wen-Chao], Zhou, W.[Wei], Zhou, T.W.[Tian-Wei],
Boundary Refinement Network for Colorectal Polyp Segmentation in Colonoscopy Images,
SPLetters(31), 2024, pp. 954-958.
IEEE DOI 2404
Feature extraction, Convolution, Image segmentation, Task analysis, Shape, Data augmentation, Colonoscopy, Deep neural network, colonoscopy image BibRef

Yu, Z.N.[Zhen-Ni], Zhao, L.[Li], Liao, T.F.[Tang-Fei], Zhang, X.Q.[Xiao-Qin], Chen, G.[Geng], Xiao, G.B.[Guo-Bao],
A novel non-pretrained deep supervision network for polyp segmentation,
PR(154), 2024, pp. 110554.
Elsevier DOI Code:
WWW Link. 2406
Polyp segmentation, Deep supervision, Attention mechanism, Global context relation BibRef

Liu, Z.Z.[Zhi-Zhe], Zheng, S.[Shuai], Sun, X.Y.[Xiao-Yi], Zhu, Z.F.[Zhen-Feng], Zhao, Y.[Yawei], Yang, X.B.[Xue-Bing], Zhao, Y.[Yao],
The Devil Is in the Boundary: Boundary-Enhanced Polyp Segmentation,
CirSysVideo(34), No. 7, July 2024, pp. 5414-5423.
IEEE DOI Code:
WWW Link. 2407
Image segmentation, Training, Task analysis, Feature extraction, Colonoscopy, Data models, Computational modeling, multi-level collaboration BibRef

Zheng, Z.R.[Zhuo-Ran], Wu, C.[Chen], Jin, Y.[Yeying], Jia, X.[Xiuyi],
Polyp-DAM: Polyp Segmentation via Depth Anything Model,
SPLetters(31), 2024, pp. 2925-2929.
IEEE DOI 2411
Image segmentation, Convolution, Image resolution, Dams, Training, Accuracy, Large models, polyp segmentation tasks, depth map BibRef

Hu, Y.B.[Yi-Biao], Jin, Y.[Yan], Jiang, Z.W.[Zhi-Wei], Zheng, Q.[Qiufu],
PVT2DNet: Polyp segmentation with vision transformer and dual decoder refinement strategy,
JVCIR(104), 2024, pp. 104304.
Elsevier DOI 2411
Polyp image segmentation, Context enhancement, Dual decoder refinement BibRef

Wang, T.[Tong], Qi, X.M.[Xiao-Ming], Yang, G.[Guanyu],
Polyp Segmentation via Semantic Enhanced Perceptual Network,
CirSysVideo(34), No. 12, December 2024, pp. 12594-12607.
IEEE DOI 2501
Semantics, Kernel, Feature extraction, Convolution, Shape, Image segmentation, Image color analysis, Polyp segmentation, feature fusion BibRef

Wang, S.[Shuai], Zhao, T.J.[Tie-Jun], Wang, G.[Guocun], Han, Y.[Ye], Wu, F.[Fan],
UCFA-Net: A U-shaped cross-fusion network with attention mechanism for enhanced polyp segmentation,
IET-IPR(19), No. 1, 2025, pp. e13293.
DOI Link 2501
medical image processing, polyp segmentation BibRef

Xiong, X.Y.[Xin-Yu], Li, W.X.[Wen-Xue], Ma, J.[Jie], Huang, D.[Duojun], Li, S.Y.[Si-Ying],
Free Meal: Boosting Semi-Supervised Polyp Segmentation by Harvesting Negative Samples,
SPLetters(32), 2025, pp. 131-135.
IEEE DOI 2501
Training, Image segmentation, Lesions, Annotations, Semisupervised learning, Biomedical imaging, Robustness, Hospitals, semi-supervised learning BibRef

Li, Z.J.[Zi-Jian], Yang, Z.Y.[Zhi-Yong], Wu, W.[Wangsheng], Guo, Z.H.[Zi-Hang], Zhu, D.D.[Dong-Dong],
Boundary Refinement Network for Polyp Segmentation With Deformable Attention,
SPLetters(32), 2025, pp. 121-125.
IEEE DOI 2501
Convolution, Feature extraction, Transformers, Kernel, Image segmentation, Computational modeling, Training, Shape, polyp segmentation BibRef

Fu, J.[Junhu], Chen, K.[Ke], Dou, Q.[Qi], Gao, Y.[Yun], He, Y.P.[Yi-Ping], Zhou, P.[Pinghong], Lin, S.L.[Sheng-Li], Wang, Y.Y.[Yuan-Yuan], Guo, Y.[Yi],
IPNet: An Interpretable Network With Progressive Loss for Whole-Stage Colorectal Disease Diagnosis,
MedImg(44), No. 2, February 2025, pp. 789-800.
IEEE DOI 2502
Lesions, Feature extraction, Ultrasonic imaging, Cancer, Classification algorithms, Accuracy, Medical diagnosis, interpretability BibRef

ELKarazle, K.[Khaled], Raman, V.[Valliappan], Chua, C.[Caslon], Then, P.[Patrick],
SCABNet: A Novel Polyp Segmentation Network With Spatial-Gradient Attention and Channel Prioritization,
IJIST(35), No. 2, 2025, pp. e70039.
DOI Link Code:
WWW Link. 2502
channel prioritization, colorectal cancer, colorectal polyp segmentation, colorectal polyps, deep learning BibRef

Nguyen, Q.V.[Quang Vinh], Vo, T.H.S.[Thanh Hoang Son], Kang, S.R.[Sae-Ryung], Kim, S.H.[Soo-Hyung],
Polyp-ses: Automatic Polyp Segmentation with Self-enriched Semantic Model,
ACCV24(II: 174-190).
Springer DOI 2412
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Chikhaoui, K.[Khalil], Alfarraj, M.[Motaz],
Advancing Colorectal Polyp Segmentation with Watershed Algorithm-Enhanced Parallel Self-Supervised Learning,
ICIP24(3124-3130)
IEEE DOI 2411
Image segmentation, Visualization, Watersheds, Self-supervised learning, Feature extraction, Data models, image reconstruction BibRef

Nguyen, P.T.[Phuong Thao], Watanabe, H.[Hiroshi],
GEEG-YOLOv8: Gaussian Enhanced Euclidean Norm Ghost Attention for Real-Time Polyp Detection,
ICIP24(3057-3063)
IEEE DOI 2411
Accuracy, Attention mechanisms, Endoscopes, Convolution, Shape, Feature extraction, Real-time systems, Attention mechanism, yolov8, medical image analysis BibRef

Chakraborti, N.[Niladri], Nayak, D.R.[Deepak Ranjan],
MCT-Net: a Lightweight Multiscale Convolutional Transformer Network for Polyp Segmentation,
ICIP24(2944-2950)
IEEE DOI 2411
Image segmentation, Accuracy, Convolution, Shape, Transformers, Feature extraction, Decoding, Polyp segmentation, transformer, cascaded attention BibRef

Yousuf, M.[Mohamed], Harb, S.[Samir], Alkabbany, I.[Islam], Ali, A.[Asem], Elshazley, S.[Salwa], Farag, A.[Aly],
Multi-View Network for Colorectal Polyps Detection in CT Colonography,
ICIP24(3051-3056)
IEEE DOI 2411
Location awareness, Sensitivity, Computed tomography, Neurons, Detectors, Colonography, Feature extraction, Colorectal cancer, segmentation BibRef

Sharma, V.[Vanshali], Kumar, A.[Abhishek], Jha, D.[Debesh], Bhuyan, M.K., Das, P.K.[Pradip K.], Bagci, U.[Ulas],
ControlPolypNet: Towards Controlled Colon Polyp Synthesis for Improved Polyp Segmentation,
EnhanceMedIm24(2325-2334)
IEEE DOI Code:
WWW Link. 2410
Image segmentation, Pathology, Shape, Source coding, Process control, Manuals, Feature extraction, Diffusion model, Colonoscopy, Polyp segmentation BibRef

Rahman, M.M.[Md Mostafijur], Munir, M.[Mustafa], Jha, D.[Debesh], Bagci, U.[Ulas], Marculescu, R.[Radu],
PP-SAM: Perturbed Prompts for Robust Adaption of Segment Anything Model for Polyp Segmentation,
DEF-AI-MIA24(4989-4995)
IEEE DOI Code:
WWW Link. 2410
Training, Image segmentation, Adaptation models, Perturbation methods, Robustness, Robustness analysis BibRef

Bui, N.T.[Nhat-Tan], Hoang, D.H.[Dinh-Hieu], Nguyen, Q.T.[Quang-Thuc], Tran, M.T.[Minh-Triet], Le, N.[Ngan],
MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation,
WACV24(7970-7979)
IEEE DOI Code:
WWW Link. 2404
Measurement, Location awareness, Technological innovation, Image segmentation, Laplace equations, Shape, Image edge detection, Image recognition and understanding BibRef

Yoo, Y.[Youngbeom], Lee, J.Y.[Jae Young], Lee, D.J.[Dong-Jae], Jeon, J.[Jiwoon], Kim, J.[Junmo],
Real-Time Polyp Detection in Colonoscopy using Lightweight Transformer,
WACV24(7794-7804)
IEEE DOI 2404
YOLO, Performance evaluation, Computational modeling, Colonoscopy, Computer architecture, Transformers, Real-time systems, Image recognition and understanding BibRef

Nam, J.H.[Ju-Hyeon], Park, S.H.[Seo-Hyeong], Syazwany, N.S.[Nur Suriza], Jung, Y.[Yerim], Im, Y.H.[Yu-Han], Lee, S.C.[Sang-Chul],
M3FPolypSegNet: Segmentation Network with Multi-Frequency Feature Fusion for Polyp Localization in Colonoscopy Images,
ICIP23(1530-1534)
IEEE DOI 2312
BibRef

Wang, A.[Ao], Wu, M.[Ming], Qi, H.[Hao], Shi, H.[Hong], Chen, J.H.[Jian-Hua], Chen, Y.R.[Yin-Ran], Luo, X.B.[Xiong-Biao],
Pyramid Transformer Driven Multibranch Fusion for Polyp Segmentation in Colonoscopic Video Images,
ICIP23(2350-2354)
IEEE DOI 2312
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Yousuf, M.[Mohamed], Alkabbany, I.[Islam], Ali, A.[Asem], Elshazley, S.[Salwa], Seow, A.[Albert], Dryden, G.[Gerald], Farag, A.[Aly],
An Automatic Colorectal Polyps Detection Approach for Ct Colonography,
ICIP23(2790-2794)
IEEE DOI 2312
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Ren, G.Y.[Guang-Yu], Lazarou, M.[Michalis], Yuan, J.[Jing], Stathaki, T.[Tania],
Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers,
VISION23(4355-4364)
IEEE DOI 2309
BibRef

Nguyen-Mau, T.H.[Trong-Hieu], Trinh, Q.H.[Quoc-Huy], Bui, N.T.[Nhat-Tan], Thi, P.T.V.[Phuoc-Thao Vo], Nguyen, M.V.[Minh-Van], Cao, X.N.[Xuan-Nam], Tran, M.T.[Minh-Triet], Nguyen, H.D.[Hai-Dang],
Pefnet: Positional Embedding Feature for Polyp Segmentation,
MMMod23(II: 240-251).
Springer DOI 2304
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Tomar, N.K.[Nikhil Kumar], Jha, D.[Debesh], Bagci, U.[Ulas],
DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation,
MMMod23(I: 334-344).
Springer DOI 2304
BibRef

Srivastava, A.[Abhishek], Chanda, S.[Sukalpa], Jha, D.[Debesh], Pal, U.[Umapada], Ali, S.[Sharib],
GMSRF-Net: An Improved generalizability with Global Multi-Scale Residual Fusion Network for Polyp Segmentation,
ICPR22(4321-4327)
IEEE DOI 2212
Training, Image segmentation, Protocols, Supervised learning, Logic gates, Performance gain BibRef

Liu, G.Q.[Guo-Qi], Zhao, M.[Manqi], Bai, L.[Lu], Guo, Z.[Zhengnan],
Cooperation of Boundary Attention and Negative Matrix L1 Regularization Loss Function for Polyp Segmentation,
ICPR22(82-88)
IEEE DOI 2212
Image segmentation, Shape, Image edge detection, Colonic polyps, Cancer, Biomedical imaging BibRef

Wu, H.[Huisi], Chen, G.L.[Gui-Lian], Wen, Z.K.[Zhen-Kun], Qin, J.[Jing],
Collaborative and Adversarial Learning of Focused and Dispersive Representations for Semi-supervised Polyp Segmentation,
ICCV21(3469-3478)
IEEE DOI 2203
Training, Representation learning, Image segmentation, Shape, Semantics, Collaboration, Medical services, Medical, biological, Vision applications and systems BibRef

Barbano, C.A.[Carlo Alberto], Perlo, D.[Daniele], Tartaglione, E.[Enzo], Fiandrotti, A.[Attilio], Bertero, L.[Luca], Cassoni, P.[Paola], Grangetto, M.[Marco],
Unitopatho, A Labeled Histopathological Dataset for Colorectal Polyps Classification and Adenoma Dysplasia Grading,
ICIP21(76-80)
IEEE DOI 2201
Deep learning, Training, Pathology, Image resolution, Annotations, Feature extraction, Deep Learning, Multi Resolution, Digital Pathology BibRef

Lan, P.N.[Phan Ngoc], An, N.S.[Nguyen Sy], Hang, D.V.[Dao Viet], Long, D.V.[Dao Van], Trung, T.Q.[Tran Quang], Thuy, N.T.[Nguyen Thi], Sang, D.V.[Dinh Viet],
NeoUNet: Towards Accurate Colon Polyp Segmentation and Neoplasm Detection,
ISVC21(II:15-28).
Springer DOI 2112
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Patel, K.[Krushi], Bur, A.M.[Andrés M.], Wang, G.H.[Guang-Hui],
Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation,
CRV21(181-188)
IEEE DOI 2108
Image segmentation, Shape, Image color analysis, Semantics, Colonoscopy, Decoding, Task analysis, Polyp segmentation, U-Net BibRef

Tomar, N.K.[Nikhil Kumar], Jha, D.[Debesh], Ali, S.[Sharib], Johansen, H.D.[Håvard D.], Johansen, D.[Dag], Riegler, M.A.[Michael A.], Halvorsen, P.[Pål],
Ddanet: Dual Decoder Attention Network for Automatic Polyp Segmentation,
EndoTect20(307-314).
Springer DOI 2103
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Galdran, A.[Adrian], Carneiro, G.[Gustavo], Ballester, M.A.G.[Miguel A. González],
Double Encoder-decoder Networks for Gastrointestinal Polyp Segmentation,
AIHA20(293-307).
Springer DOI 2103
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Mulliqi, N., Yildirim, S., Mohammed, A., Ahmedi, L., Wang, H., Elezaj, O., Hovde, Ø.,
The Importance Of Skip Connections In Encoder-Decoder Architectures For Colorectal Polyp Detection,
ICIP20(380-384)
IEEE DOI 2011
Image segmentation, Computer architecture, Colonoscopy, Cancer, Indexes, Colon, Feature extraction, Colorectal polyps, U-Net++ BibRef

Jha, D.[Debesh], Smedsrud, P.H.[Pia H.], Riegler, M.A.[Michael A.], Halvorsen, P.[Pål], de Lange, T.[Thomas], Johansen, D.[Dag], Johansen, H.D.[Håvard D.],
Kvasir-seg: A Segmented Polyp Dataset,
MMMod20(II:451-462).
Springer DOI 2003
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van Grinsven, M.C.A., Scheeve, T., Schreuder, R., van der Sommen, F., Schoon, E.J., de With, P.H.N.,
Image Features for Automated Colorectal Polyp Classification Based on Clinical Prediction Models,
ICIP19(210-214)
IEEE DOI 1910
Colorectal polyps, quantitative features, computer-aided diagnosis, SVM, gastroenterology BibRef

Mo, X., Tao, K., Wang, Q., Wang, G.,
An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN,
ICPR18(3929-3934)
IEEE DOI 1812
Proposals, Videos, Training, Cancer, Head, Detectors BibRef

Souaidi, M., Charfi, S., Abdelouahad, A.A., El Ansari, M.,
New features for wireless capsule endoscopy polyp detection,
ISCV18(1-6)
IEEE DOI 1807
Gabor filters, curvelet transforms, discrete wavelet transforms, diseases, endoscopes, feature extraction, medical image processing, wireless capsule endoscopy (WCE) BibRef

Taha, B., Dias, J., Werghi, N.,
Convolutional neural networkasa feature extractor for automatic polyp detection,
ICIP17(2060-2064)
IEEE DOI 1803
Cancer, Computer architecture, Convolution, Databases, Feature extraction, Support vector machines, Training, feature extractor BibRef

Wichakam, I.[Itsara], Panboonyuen, T.[Teerapong], Udomcharoenchaikit, C.[Can], Vateekul, P.[Peerapon],
Real-Time Polyps Segmentation for Colonoscopy Video Frames Using Compressed Fully Convolutional Network,
MMMod18(I:393-404).
Springer DOI 1802
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Angermann, Q.[Quentin], Bernal, J.[Jorge], Sánchez-Montes, C.[Cristina], Hammami, M.[Maroua], Fernández-Esparrach, G.[Gloria], Dray, X.[Xavier], Romain, O.[Olivier], Sánchez, F.J.[F. Javier], Histace, A.[Aymeric],
Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis,
CARE17(29-41).
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Korbar, B., Olofson, A.M., Miraflor, A.P., Nicka, C.M., Suriawinata, M.A., Torresani, L., Suriawinata, A.A., Hassanpour, S.,
Looking Under the Hood: Deep Neural Network Visualization to Interpret Whole-Slide Image Analysis Outcomes for Colorectal Polyps,
Microscopy17(821-827)
IEEE DOI 1709
Agriculture, Backpropagation, Cancer, Computer architecture, Neural networks, Training, Visualization BibRef

Ribeiro, E.[Eduardo], Uhl, A.[Andreas], Wimmer, G.[Georg], Häfner, M.[Michael],
Transfer Learning for Colonic Polyp Classification Using Off-the-Shelf CNN Features,
CARE16(1-13).
Springer DOI 1703
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Wimmer, G.[Georg], Gadermayr, M.[Michael], Kwitt, R.[Roland], Häfner, M.[Michael], Merhof, D.[Dorit], Uhl, A.[Andreas],
Evaluation of i-Scan Virtual Chromoendoscopy and Traditional Chromoendoscopy for the Automated Diagnosis of Colonic Polyps,
CARE16(59-71).
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Gil, D.[Debora], Sánchez, F.J.[F. Javier], Fernández-Esparrach, G.[Gloria], Bernal, J.[Jorge],
3D Stable Spatio-Temporal Polyp Localization in Colonoscopy Videos,
CARE15(140-152).
WWW Link. 1605
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Wimmer, G., Uhl, A., Häfner, M.[Michael],
A novel filterbank especially designed for the classification of colonic polyps,
ICPR16(2150-2155)
IEEE DOI 1705
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Earlier: A2, A1, A3:
Shape and size adapted local fractal dimension for the classification of polyps in HD colonoscopy,
ICIP14(2299-2303)
IEEE DOI 1502
Colonic polyps, Databases, Endoscopes, Feature extraction, Gaussian distribution, Histograms. Colon BibRef

Hafner, M., Liedlgruber, M., Uhl, A.[Andreas], Wimmer, G.,
Bridging the Resolution Gap between Endoscope Types for a Colonic Polyp Classification,
ICPR14(2739-2744)
IEEE DOI 1412
Endoscopes BibRef

Fiori, M.[Marcelo], Musé, P.[Pablo], Sapiro, G.[Guillermo],
Polyps Flagging in Virtual Colonoscopy,
CIARP13(II:181-189).
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Xu, H.Y.[Hai-Yong], Gage, H.D.[H. Donald], Santago, P.[Pete], Ge, Y.R.[Yao-Rong],
Colorectal Polyp Segmentation Based on Geodesic Active Contours with a Shape-Prior Model,
VirtualColon10(134-140).
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Iwahori, Y.[Yuji], Shinohara, T.[Takayuki], Hattori, A.[Akira], Woodham, R.J.[Robert J.], Fukui, S.[Shinji], Bhuyan, M.K., Kasugai, K.[Kunio],
Automatic Polyp Detection in Endoscope Images Using a Hessian Filter,
MVA13(xx-yy). Award, MVA Decade. BibRef 1300

Tsuda, S.[Seiya], Iwahori, Y.J.[Yu-Ji], Hanai, Y.[Yuki], Woodham, R.J.[Robert J.], Bhuyan, M.K., Kasugai, K.[Kunio],
Recovering size and shape of polyp from endoscope image by RBF-NN modification,
ICIP15(4684-4688)
IEEE DOI 1512
Endoscope Image; RBF-NN; Regression Analysis; Size and Shape; VBW Model BibRef

Häfner, M.[Michael], Uhl, A.[Andreas], Wimmer, G.[Georg],
A Novel Shape Feature Descriptor for the Classification of Polyps in HD Colonoscopy,
MCV13(205-213).
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Condessa, F.[Filipe], Bioucas-Dias, J.[José],
Segmentation and Detection of Colorectal Polyps Using Local Polynomial Approximation,
ICIAR12(II: 188-197).
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Park, M.[Mira], Jin, J.S.[Jesse S.], Summons, P.[Peter], Luo, S.[Suhuai], Hofstetter, R.[Robert],
False Positive Reduction in Colonic Polyp Detection Using Glocal Information,
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Chang, K.W.[Kevin W.], Liu, J.[Jiamin], Yao, J.H.[Jian-Hua], Summers, R.M.[Ronald M.],
Improved method for predicting polyp location from CT colonography for optical colonoscopy,
ICIP10(4365-4368).
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Chen, D.Q.[Dong-Qing], Farag, A.A.[Aly A.], Falk, R.L.[Robert L.], Dryden, G.W.[Gerald W.],
A variational framework for 3D colonic polyp visualization in virtual colonoscopy,
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Park, M., Jesse, J.S., Hofstetter, R., Luo, S., Summons, P.,
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Chen, D.Q.[Dong-Qing], Farag, A.A.[Aly A.], Falk, R.L.[Robert L.], Dryden, G.W.[Gerald W.],
Gaussian curvature flowmodel for colonic polyp detection in CT colonography,
ICIP08(2988-2991).
IEEE DOI 0810
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Shahbazi, M.[Mozhdeh], Sattari, F.M.[F. Mehran], Ghazi, M.[Mojtaba],
Automatic Polyp Detection from CT Colonography Using Mathematical Morphology,
ISPRS08(B5: 823 ff).
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Lu, L.[Le], Barbu, A.[Adrian], Wolf, M.[Matthias], Liang, J.M.[Jian-Ming], Salganicoff, M.[Marcos], Comaniciu, D.[Dorin],
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IEEE DOI 0806
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Tu, Z.W.[Zhuo-Wen], Zhou, X.S.[Xiang Sean], Bogoni, L.[Luca], Barbu, A.[Adrian], Comaniciu, D.[Dorin],
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IEEE DOI 0606
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Chen, D.Q.[Dong-Qing], Farag, A.A.[Aly A.], Hassouna, M.S.[M. Sabry], Falk, R.[Robert], Dryden, G.W.[Gerald W.],
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Note on Feature Selection for Polyp Detection in CT Colonography,
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IEEE DOI 0609
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