21.14.3 Medical Applications -- Colonoscopy, Colon Cancer

Haker, S., Angenent, S., Tannenbaum, A., Kikinis, R.,
Nondistorting flattening maps and the 3-D visualization of colon CT images,
MedImg(19), No. 7, July 2000, pp. 665-670.
IEEE Top Reference. 0110
BibRef
And: A1, A2, A3, Only: Correction: MedImg(19), No. 12, December 2000, pp. 1267. 0110

See also On the Laplace-Beltrami operator and brain surface flattening.
See also Flattening Maps for the Visualization of Multibranched Vessels. BibRef

Chen, D., Wax, M.R., Li, L., Liang, Z., Li, B., Kaufman, A.E.,
A novel approach to extract colon lumen from CT images for virtual colonoscopy,
MedImg(19), No. 12, December 2000, pp. 1220-1226.
IEEE Top Reference. 0110
BibRef

Wan, M.[Ming], Liang, Z.R.[Zheng-Rong], Ke, Q.[Qi], Hong, L.C.[Li-Chan], Bitter, I., Kaufman, A.,
Automatic centerline extraction for virtual colonoscopy,
MedImg(21), No. 12, December 2002, pp. 1450-1460.
IEEE Top Reference. 0301
BibRef

Zhu, H.B.[Hong-Bin], Li, L.H.[Li-Hong], Fan, Y.[Yi], Liang, Z.R.[Zheng-Rong],
Haustral Fold Segmentation of CT Colonography Using Ridge Line Detection,
VirtualColon10(24-31).
Springer DOI 1112
BibRef

Zhu, H.B.[Hong-Bin], Fan, Y.[Yi], Liang, Z.R.[Zheng-Rong],
Improved Curvature Estimation for Shape Analysis in Computer-Aided Detection of Colonic Polyps,
VirtualColon10(9-14).
Springer DOI 1112
BibRef

Zalis, M.E., Perumpillichira, J., Hahn, P.F.,
Digital Subtraction Bowel Cleansing for CT Colonography Using Morphological and Linear Filtration Methods,
MedImg(23), No. 11, November 2004, pp. 1335-1343.
IEEE Abstract. 0411
BibRef

Franaszek, M., Summers, R.M., Pickhardt, P.J., Choi, J.R.,
Hybrid segmentation of colon filled with air and opacified fluid for CT colonography,
MedImg(25), No. 3, March 2006, pp. 358-368.
IEEE DOI 0604
BibRef

Kang, D.G., Ra, J.B.,
A New Path Planning Algorithm for Maximizing Visibility in Computed Tomography Colonography,
MedImg(24), No. 8, August 2005, pp. 957-968.
IEEE DOI 0508
BibRef

van Uitert, R.L., Summers, R.M.,
Automatic Correction of Level Set Based Subvoxel Precise Centerlines for Virtual Colonoscopy Using the Colon Outer Wall,
MedImg(26), No. 8, August 2007, pp. 1069-1078.
IEEE DOI 0709
BibRef

Jeong, S.J.[Seok-Jae], Kaufman, A.E.[Arie E.],
Interactive wireless virtual colonoscopy,
VC(23), No. 7, July 2007, pp. 545-557.
Springer DOI 0707
BibRef

Gurijala, K.C.[Krishna Chaitanya], Kaufman, A.E.[Arie E.], Zeng, W.[Wei], Gu, X.F.[Xian-Feng],
Extraction of Landmarks and Features from Virtual Colon Models,
VirtualColon10(105-112).
Springer DOI 1112
BibRef

Zeng, W.[Wei], Marino, J.[Joseph], Gu, X.F.[Xian-Feng], Kaufman, A.E.[Arie E.],
Conformal Geometry Based Supine and Prone Colon Registration,
VirtualColon10(113-119).
Springer DOI 1112
BibRef

Marino, J.[Joseph], Kaufman, A.E.[Arie E.],
Colon Visualization Using Shape Preserving Flattening,
VirtualColon10(120-125).
Springer DOI 1112
BibRef

Tosun, A.B.[Akif Burak], Kandemir, M.[Melih], Sokmensuer, C.[Cenk], Gunduz-Demir, C.[Cigdem],
Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection,
PR(42), No. 6, June 2009, pp. 1104-1112.
Elsevier DOI 0902
Cancer detection; Image segmentation; Histopathological image analysis; Object-oriented texture; Colon adenocarcinoma BibRef

Tosun, A.B.[Akif Burak], Gunduz-Demir, C.[Cigdem],
Graph Run-Length Matrices for Histopathological Image Segmentation,
MedImg(30), No. 3, March 2011, pp. 721-732.
IEEE DOI 1103
BibRef

Tosun, A.B.[Akif Burak], Sokmensuer, C.[Cenk], Gunduz-Demir, C.[Cigdem],
Unsupervised Tissue Image Segmentation through Object-Oriented Texture,
ICPR10(2516-2519).
IEEE DOI 1008
BibRef

Schlachter, M., Reisert, M., Herz, C., Schlurmann, F., Lassmann, S., Werner, M., Burkhardt, H., Ronneberger, O.,
Harmonic Filters for 3D Multichannel Data: Rotation Invariant Detection of Mitoses in Colorectal Cancer,
MedImg(29), No. 8, August 2010, pp. 1485-1495.
IEEE DOI 1008

See also Harmonic Filters for Generic Feature Detection in 3D. BibRef

de Visser, H.[Hans], Passenger, J.[Josh], Conlan, D.[David], Russ, C.[Christoph], Hellier, D.[David], Cheng, M.[Mario], Acosta, O.[Oscar], Ourselin, S.[SÉbastien], Salvado, O.[Olivier],
Developing A Next Generation Colonoscopy Simulator,
IJIG(10), No. 2, April 2010, pp. 203-217.
DOI Link 1003
BibRef

Suzuki, K.[Kenji],
Machine Learning in Computer-Aided Diagnosis of the Thorax and Colon in CT: A Survey,
IEICE(E96-D), No. 4, April 2013, pp. 772-783.
WWW Link. 1304
Survey, Thorax. Survey, Colon. BibRef

Cai, W.L.[Wen-Li], Lee, J.G., Zalis, M.E., Yoshida, H.[Hiroyuki],
Mosaic Decomposition: An Electronic Cleansing Method for Inhomogeneously Tagged Regions in Noncathartic CT Colonography,
MedImg(30), No. 3, March 2011, pp. 559-574.
IEEE DOI 1103
BibRef

Cai, W.L.[Wen-Li], Yoshida, H.[Hiroyuki],
Electronic Cleansing in CT Colonography: Past, Present, and Future,
VirtualColon10(1-8).
Springer DOI 1112
BibRef

Chang, J.[Jian], Yang, X.S.[Xiao-Song], Pan, J.J.[Jun J.], Li, W.X.[Wen-Xi], Zhang, J.J.[Jian J.],
A fast hybrid computation model for rectum deformation,
VC(27), No. 2, February 2011, pp. 97-107.
WWW Link. 1103
BibRef

Filip, D., Yadid-Pecht, O., Andrews, C.N., Mintchev, M.P.,
Self-Stabilizing Colonic Capsule Endoscopy: Pilot Study of Acute Canine Models,
MedImg(30), No. 12, December 2011, pp. 2115-2125.
IEEE DOI 1112
BibRef

Wang, S., McKenna, M.T., Nguyen, T.B., Burns, J.E., Petrick, N., Sahiner, B., Summers, R.M.,
Seeing Is Believing: Video Classification for Computed Tomographic Colonography Using Multiple-Instance Learning,
MedImg(31), No. 5, May 2012, pp. 1141-1153.
IEEE DOI 1202
BibRef

Masood, K., Rajpoot, N.,
Spatial Analysis for Colon Biopsy Classification from Hyperspectral Imagery,
BMVA(2008), No. 4, 2008, pp. 1-15.
PDF File. 1209
BibRef

Nawaz, T.[Tahir], Slabaugh, G.G.[Greg G.],
A bottom-up approach for the analysis of haustral fold ridges in CTC-CAD,
BMVA(2012), No. 8, 2012, pp. 1-15.
PDF File. 1209
BibRef

Claridge, E., Hidovic-Rowe, D.,
Model Based Inversion for Deriving Maps of Histological Parameters Characteristic of Cancer From Ex-Vivo Multispectral Images of the Colon,
MedImg(33), No. 4, April 2014, pp. 822-835.
IEEE DOI 1404
Blood BibRef

Ismail, M., Elshzaly, S., Farag, A., Sites, C., Curtin, R., Falk, R., Seow, A., Dryden, G.,
Revamped fly-over for accurate colon visualisation in virtual colonoscopy,
IET-CV(9), No. 4, 2015, pp. 511-521.
DOI Link 1509
cameras BibRef

Sirinukunwattana, K., Snead, D.R.J., Rajpoot, N.M.,
A Stochastic Polygons Model for Glandular Structures in Colon Histology Images,
MedImg(34), No. 11, November 2015, pp. 2366-2378.
IEEE DOI 1511
Bayes methods BibRef

Tchiotsop, D.[Daniel], Tchinda, B.S.[Beaudelaire Saha], Tchinda, R.[René], Kenné, G.[Godpromesse],
Edge detection of intestinal parasites in stool microscopic images using multi-scale wavelet transform,
SIViP(9), No. 1 Supp, December 2015, pp. 121-134.
Springer DOI 1601
BibRef

Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R.J., Cree, I.A., Rajpoot, N.M.,
Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images,
MedImg(35), No. 5, May 2016, pp. 1196-1206.
IEEE DOI 1605
Cancer BibRef

Mesejo, P., Pizarro, D., Abergel, A., Rouquette, O., Beorchia, S., Poincloux, L., Bartoli, A.,
Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy,
MedImg(35), No. 9, September 2016, pp. 2051-2063.
IEEE DOI 1609
Biopsy BibRef

Boroomand, A., Shafiee, M.J., Khalvati, F., Haider, M.A., Wong, A.,
Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model,
MedImg(35), No. 12, December 2016, pp. 2587-2597.
IEEE DOI 1612
Coils BibRef

Sánchez, F.J.[F. Javier], Bernal, J.[Jorge], Sánchez-Montes, C.[Cristina], de Miguel, C.R.[Cristina Rodríguez], Fernández-Esparrach, G.[Gloria],
Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos,
MVA(28), No. 8, November 2017, pp. 917-936.
Springer DOI 1710
BibRef

Apeke, S., Gaubert, L., Boussion, N., Lambin, P., Visvikis, D., Rodin, V., Redou, P.,
Multi-Scale Modeling and Oxygen Impact on Tumor Temporal Evolution: Application on Rectal Cancer During Radiotherapy,
MedImg(37), No. 4, April 2018, pp. 871-880.
IEEE DOI 1804
Biological system modeling, Computational modeling, Evolution (biology), Microscopy, Predictive models, Solid modeling, tumor BibRef

Ahn, J., Nguyen Loc, H., Krishna Balan, R., Lee, Y., Ko, J.,
Finding Small-Bowel Lesions: Challenges in Endoscopy-Image-Based Learning Systems,
Computer(51), No. 5, May 2018, pp. 68-76.
IEEE DOI 1806
Biomedical monitoring, Computational modeling, Diseases, Endoscopes, Lesions, Real-time systems, CNNs, GANs, bioinformatics, mobile and embedded deep learning BibRef

Sari, C.T., Gunduz-Demir, C.,
Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images,
MedImg(38), No. 5, May 2019, pp. 1139-1149.
IEEE DOI 1905
Feature extraction, Image segmentation, Cancer, Task analysis, Pathology, Quantization (signal), Deep learning, feature learning, hematoxylin-eosin staining BibRef

Kim, J.[Jongin], Oh, J.E.[Ji Eun], Lee, J.[Joohyung], Kim, M.J.[Min Ju], Hur, B.Y.[Bo Yun], Sohn, D.K.[Dae Kyung], Lee, B.[Boreom],
Rectal cancer: Toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural network,
IJIST(29), No. 3, September 2019, pp. 247-259.
DOI Link 1908
BibRef

Aghanouri, M.[Mehrnaz], Ghaffari, A.[Ali], Serej, N.D.[Nasim Dadashi], Rabbani, H.[Hossein], Adibi, P.[Peyman],
New image-guided method for localisation of an active capsule endoscope in the stomach,
IET-IPR(13), No. 12, October 2019, pp. 2321-2327.
DOI Link 1911
BibRef

Saini, S.K.[Sumindar Kaur], Bansal, V.[Vasudha], Kaur, R.[Ravinder], Juneja, M.[Mamta],
ColpoNet for automated cervical cancer screening using colposcopy images,
MVA(31), No. 3, March 2020, pp. Article15.
Springer DOI 2004
BibRef

Shaban, M.[Muhammad], Awan, R.[Ruqayya], Fraz, M.M.[Muhammad Moazam], Azam, A.[Ayesha], Tsang, Y.W.[Yee-Wah], Snead, D.[David], Rajpoot, N.M.[Nasir M.],
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images,
MedImg(39), No. 7, July 2020, pp. 2395-2405.
IEEE DOI 2007
Feature extraction, Cancer, Image segmentation, Tumors, Glands, Image resolution, Organizations, Computational pathology, cancer grading BibRef

Zhou, Y.N.[Yan-Ning], Graham, S.[Simon], Koohbanani, N.A.[Navid Alemi], Shaban, M.[Muhammad], Heng, P.A.[Pheng-Ann], Rajpoot, N.M.[Nasir M.],
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images,
VRMI19(388-398)
IEEE DOI 2004
biological organs, biological tissues, cancer, cellular biophysics, convolutional neural nets, feature extraction, graph theory, deep learning BibRef

Javed, S., Mahmood, A., Werghi, N., Benes, K., Rajpoot, N.,
Multiplex Cellular Communities in Multi-Gigapixel Colorectal Cancer Histology Images for Tissue Phenotyping,
IP(29), 2020, pp. 9204-9219.
IEEE DOI 2010
Cancer, Multiplexing, Feature extraction, Tumors, Biological tissues, Pathology, Cellular community detection, tissue phenotyping, colon cancer BibRef

Freedman, D., Blau, Y., Katzir, L., Aides, A., Shimshoni, I., Veikherman, D., Golany, T., Gordon, A., Corrado, G., Matias, Y., Rivlin, E.,
Detecting Deficient Coverage in Colonoscopies,
MedImg(39), No. 11, November 2020, pp. 3451-3462.
IEEE DOI 2011
Colonoscopy, Colon, Estimation, Cancer, Machine learning, Streaming media, Real-time systems, Colonoscopy, coverage, unsupervised deep learning BibRef

Nirmalakumari, K., Rajaguru, H.[Harikumar], Rajkumar, P.,
Performance analysis of classifiers for colon cancer detection from dimensionality reduced microarray gene data,
IJIST(30), No. 4, 2020, pp. 1012-1032.
DOI Link 2011
colon cancer dataset, discriminant classifiers, fuzzy C-means, microarray, particle swarm optimization, regression classifiers BibRef

Kim, J., Lew, H.M., Kim, J.H., Youn, S., Faruque, H.A., Seo, A.N., Park, S.Y., Chang, J.H., Kim, E., Hwang, J.Y.,
Forward-Looking Multimodal Endoscopic System Based on Optical Multispectral and High-Frequency Ultrasound Imaging Techniques for Tumor Detection,
MedImg(40), No. 2, February 2021, pp. 594-606.
IEEE DOI 2102
Tumors, Ultrasonic imaging, Optical imaging, Biomedical optical imaging, Probes, Optical sensors, colorectal tumor BibRef

Babu, T.[Tina], Singh, T.[Tripty], Gupta, D.[Deepa],
Colon cancer prediction using 2DReCA segmentation and hybrid features on histopathology images,
IET-IPR(14), No. 16, 19 December 2020, pp. 4144-4157.
DOI Link 2103
BibRef

Dong, J.H.[Jia-Hua], Cong, Y.[Yang], Sun, G.[Gan], Yang, Y.S.[Yun-Sheng], Xu, X.W.[Xiao-Wei], Ding, Z.M.[Zheng-Ming],
Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation,
CirSysVideo(31), No. 5, 2021, pp. 2020-2033.
IEEE DOI 2105
BibRef

Dong, J.H.[Jia-Hua], Cong, Y.[Yang], Sun, G.[Gan], Hou, D.,
Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation,
ICCV19(10711-10720)
IEEE DOI 2004
diseases, endoscopes, image classification, image representation, image segmentation, iterative methods, Solid modeling BibRef

Nirmalakumari, K., Rajaguru, H.[Harikumar],
Enhancing the performance of hybrid evolutionary algorithms in microarray colon data classification,
IJIST(31), No. 3, 2021, pp. 1595-1608.
DOI Link 2108
artificial bee colony, expectation maximization, firefly, microarray colon cancer, particle swarm optimization, power spectral density BibRef

Huang, Y.J.[Yi-Jie], Dou, Q.[Qi], Wang, Z.X.[Zi-Xian], Liu, L.Z.[Li-Zhi], Jin, Y.[Ying], Li, C.F.[Chao-Feng], Wang, L.S.[Li-Sheng], Chen, H.[Hao], Xu, R.H.[Rui-Hua],
3-D RoI-Aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation,
Cyber(51), No. 11, November 2021, pp. 5397-5408.
IEEE DOI 2112
Image segmentation, Feature extraction, Cancer, Graphics processing units, Task analysis, Solid modeling, Tumors, tumor segmentation BibRef

Jiang, Y.K.[Yan-Kai], Xu, S.F.[Shu-Feng], Fan, H.J.[Hong-Jie], Qian, J.H.[Jia-Hong], Luo, W.Z.[Wei-Zhi], Zhen, S.H.[Shi-Hui], Tao, Y.[Yubo], Sun, J.H.[Ji-Hong], Lin, H.[Hai],
ALA-Net: Adaptive Lesion-Aware Attention Network for 3D Colorectal Tumor Segmentation,
MedImg(40), No. 12, December 2021, pp. 3627-3640.
IEEE DOI 2112
Image segmentation, Task analysis, Semantics, Convolution, Spatial resolution, Location awareness, colorectal tumor segmentation BibRef

Srivastava, G.[Gargi], Srivastava, R.[Rajeev],
Colon tumor localization using three input variants to Faster Region-based Convolutional Neural Network and lazy snapping,
IJIST(31), No. 4, 2021, pp. 2123-2135.
DOI Link 2112
colon tumor detection, deep learning, medical image processing BibRef

Khadilkar, S.P.[Samrat Pundalik],
Colon Cancer Detection Using Hybrid Features and Genetically Optimized Neural Network Classifier,
IJIG(22), No. 2, April 2022, pp. 2250024.
DOI Link 2205
BibRef

Wang, C.[Chong], Cui, Z.M.[Zhi-Ming], Yang, J.W.[Jun-Wei], Han, M.[Miaofei], Carneiro, G.[Gustavo], Shen, D.G.[Ding-Gang],
BowelNet: Joint Semantic-Geometric Ensemble Learning for Bowel Segmentation From Both Partially and Fully Labeled CT Images,
MedImg(42), No. 4, April 2023, pp. 1225-1236.
IEEE DOI 2304
Image segmentation, Computed tomography, Colon, Skeleton, Shape, Cancer, Task analysis, Bowel segmentation, ensemble learning, partially labeled datasets BibRef

Nergiz, M.[Mehmet],
Federated learning-based colorectal cancer classification by convolutional neural networks and general visual representation learning,
IJIST(33), No. 3, 2023, pp. 951-964.
DOI Link 2305
big transfer, colorectal cancer, computational pathology, convolutional neural network, federated learning, general visual representation learning BibRef

Cui, Z.W.[Zhi-Wei], Li, Y.[Yehui], Li, W.[Weibing], Li, J.X.[Ji-Xiu], Chiu, P.W.Y.[Philip Wai Yan], Li, Z.[Zheng],
Hybrid Vision/Magnetic-Force Finite-Time Convergent Neural Network Tracking Control of Electromagnetically Actuated Soft-Tethered Colonoscope Robot With Current Constraints,
SMCS(53), No. 10, October 2023, pp. 6080-6093.
IEEE DOI 2310
BibRef

Kaçmaz, R.N.[Rukiye Nur], Dogan, R.S.[Refika Sultan], Yilmaz, B.[Bülent],
A comprehensive study on automatic non-informative frame detection in colonoscopy videos,
IJIST(34), No. 1, 2024, pp. e23017.
DOI Link 2401
colonoscopy, feature extraction, image processing, machine learning, transfer learning BibRef

Natarajan, T.[Thendral], Devan, L.[Lakshmi], Seethapathy, R.P.[Ramaprabha Palayanoor], Balakrishnan, S.K.[Senthil Kumar],
A customized ConvNeXt-XL network with fusion of deep and handcrafted features for colposcopy image classification,
IJIST(34), No. 2, 2024, pp. e23036.
DOI Link 2402
cervical cancer, colposcopy, ConvNeXt-XL, deep convolutional neural networks, feature concatenation BibRef

Sadeghi, V.[Vahid], Mehridehnavi, A.[Alireza], Sanahmadi, Y.[Yasaman], Rakhshani, S.[Sajed], Omrani, M.[Mina], Sharifi, M.[Mohsen],
Real-time small bowel visualization quality assessment in wireless capsule endoscopy images using different lightweight embeddable models,
IJIST(34), No. 3, 2024, pp. e23069.
DOI Link 2404
clean and contaminated, embedded image segmentation, low power consumption, wireless capsule endoscopy (WCE) BibRef

Zhang, H.M.[Hui-Min], Ren, M.Y.[Ming-Yang], Wang, Y.[Yu], Jin, Z.Y.[Zhi-Yuan], Zhang, S.X.[Shan-Xiang], Liu, J.Q.[Jia-Qian], Fu, J.[Jia], Qin, H.[Huan],
In Vivo Microwave-Induced Thermoacoustic Endoscopy for Colorectal Tumor Detection in Deep Tissue,
MedImg(43), No. 4, April 2024, pp. 1619-1627.
IEEE DOI 2404
Microwave imaging, Microwave antennas, Microwave amplifiers, Probes, Imaging, Tumors, Endoscopes, internal organs BibRef

Figueredo, W.K.R.[Weslley Kelson Ribeiro], Silva, A.C.[Aristófanes Corrêa], de Paiva, A.C.[Anselmo Cardoso], Diniz, J.O.B.[João Otávio Bandeira], Brandão, A.[Alice], Oliveira, M.A.P.[Marco Aurelio Pinho],
Automatic segmentation of deep endometriosis in the rectosigmoid using deep learning,
IVC(151), 2024, pp. 105261.
Elsevier DOI 2411
Endometriosis, Magnetic resonance, Classification, Segmentation, Deep learning BibRef

Kelner, O.[Ori], Weinstein, O.[Or], Rivlin, E.[Ehud], Goldenberg, R.[Roman],
Semantic Parsing of Colonoscopy Videos with Multi-Label Temporal Networks,
CVAMD23(2591-2598)
IEEE DOI 2401
BibRef

Kligvasser, I.[Idan], Leifman, G.[George], Goldenberg, R.[Roman], Rivlin, E.[Ehud], Elad, M.[Michael],
Semi-supervised Quality Evaluation of Colonoscopy Procedures,
CVAMD23(2347-2355)
IEEE DOI 2401
BibRef

Carcagnì, P.[Pierluigi], Leo, M.[Marco], Signore, L.[Luca], Distante, C.[Cosimo],
Medical Transformers for Boosting Automatic Grading of Colon Carcinoma in Histological Images,
CIAP23(I:135-146).
Springer DOI 2312
BibRef

Katzir, L.[Liran], Veikherman, D.[Danny], Dashinsky, V.[Valentin], Goldenberg, R.[Roman], Shimshoni, I.[Ilan], Rabani, N.[Nadav], Cohen, R.[Regev], Kelner, O.[Ori], Rivlin, E.[Ehud], Freedman, D.[Daniel],
Estimating Withdrawal Time in Colonoscopies,
MCV22(495-512).
Springer DOI 2304
BibRef

Vuong, T.T.L.[Trinh Thi Le], Vu, Q.D.[Quoc Dang], Jahanifar, M.[Mostafa], Graham, S.[Simon], Kwak, J.T.[Jin Tae], Rajpoot, N.[Nasir],
Impash: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification,
MCV22(543-555).
Springer DOI 2304
BibRef

Canfora, I.[Ilaria], Cutaia, G.[Giuseppe], Marcianò, M.[Marco], Calamia, M.[Mauro], Faraone, R.[Roberta], Cannella, R.[Roberto], Benfante, V.[Viviana], Comelli, A.[Albert], Guercio, G.[Giovanni], Giuseppe, L.R.[Lo Re], Salvaggio, G.[Giuseppe],
A Predictive System to Classify Preoperative Grading of Rectal Cancer Using Radiomics Features,
AIRCAD22(431-440).
Springer DOI 2208
BibRef

Touchanti, K.[Kaouthar], Ezzazi, I.[Imad], El Bekkali, M.[Moulhime], Maser, S.[Said],
A 2-stages feature selection framework for colon cancer classification using SVM,
ISCV22(1-5)
IEEE DOI 2208
Support vector machines, Training, Machine learning algorithms, Redundancy, Machine learning, Feature extraction, Colon, Dimensionality reduction BibRef

Leo, M.[Marco], Cacagnì, P.[Pierluigi], Signore, L.[Luca], Benincasa, G.[Giulio], Laukkanen, M.O.[Mikko O.], Distante, C.[Cosimo],
Improving Colon Carcinoma Grading by Advanced CNN Models,
CIAP22(I:233-244).
Springer DOI 2205
BibRef

Rahman, M.M.[Md Marufi], Oh, J.[JungHwan], Tavanapong, W.[Wallapak], Wong, J.[Johnny], de Groen, P.C.[Piet C.],
Automated Bite-block Detection to Distinguish Colonoscopy from Upper Endoscopy Using Deep Learning,
ISVC21(II:216-228).
Springer DOI 2112
BibRef

Blau, Y.[Yochai], Freedman, D.[Daniel], Dashinsky, V.[Valentin], Goldenberg, R.[Roman], Rivlin, E.[Ehud],
Unsupervised 3D Shape Coverage Estimation with Applications to Colonoscopy,
CVAMD21(3364-3374)
IEEE DOI 2112
Shape, Estimation, Colonoscopy, Noise measurement BibRef

Liu, X.F.[Xue-Feng], Wang, H.[Hao], Chen, S.[Shuang], Liu, S.L.[Shun-Li], Zhang, Z.X.[Zai-Xin], Fu, M.[Min],
Segmentation and Identification of Small Intestinal Lesions Based on U-Net with Batch Normalization and Virtual Sample,
ICIVC21(134-139)
IEEE DOI 2112
Training, Image segmentation, Intestines, Image coding, Hospitals, Computed tomography, Decoding, deep learning, small samples, small intestine non-lymphoma BibRef

Bozorgtabar, B.[Behzad], Vray, G.[Guillaume], Mahapatra, D.[Dwarikanath], Thiran, J.P.[Jean-Philippe],
SOoD: Self-Supervised Out-of-Distribution Detection Under Domain Shift for Multi-Class Colorectal Cancer Tissue Types,
CVAMD21(3317-3326)
IEEE DOI 2112
Training, Deep learning, Histopathology, Semantics, Detectors, Entropy BibRef

Graham, S.[Simon], Jahanifar, M.[Mostafa], Azam, A.[Ayesha], Nimir, M.[Mohammed], Tsang, Y.W.[Yee-Wah], Dodd, K.[Katherine], Hero, E.[Emily], Sahota, H.[Harvir], Tank, A.[Atisha], Benes, K.[Ksenija], Wahab, N.[Noorul], Minhas, F.[Fayyaz], Raza, S.E.A.[Shan E. Ahmed], El Daly, H.[Hesham], Gopalakrishnan, K.[Kishore], Snead, D.[David], Rajpoot, N.[Nasir],
Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification,
CDPath21(684-693)
IEEE DOI 2112
Deep learning, Image segmentation, Image analysis, Annotations, Histopathology, Computational modeling, Biological system modeling BibRef

Dutta, A.[Amartya], Bhattacharjee, R.K.[Rajat Kanti], Barbhuiya, F.A.[Ferdous Ahmed],
Efficient Detection of Lesions During Endoscopy,
EndoTect20(315-322).
Springer DOI 2103
BibRef

Ke, J., Shen, Y., Guo, Y., Liang, X.,
Fast Tumor Detector in Whole-Slide Image With Dynamic Programing Based Monte Carlo Sampling,
ICIP20(2471-2475)
IEEE DOI 2011
Tumors, Monte Carlo methods, Convergence, Cancer, Mathematical model, Pathology, Machine learning, Tumor location, Colorectal cancer, Deep learning BibRef

Mathew, S., Nadeem, S., Kumari, S., Kaufman, A.,
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Colon, Image reconstruction, Cancer, Endoscopes, Machine learning BibRef

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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Colonoscopy, Polyp Detection, Analysis .