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Ensemble of Instance Segmentation Models for Polyp Segmentation in Colonoscopy Images

Jaeyong Kang, Jeonghwan Gwak
2019 IEEE Access  
Thus, we are primarily motivated by the need for obtaining an early and accurate diagnosis of polyps in the colonoscopy images.  ...  Although colonoscopy is regarded as the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the level of hand-eye coordination and the operator skills  ...  Several research groups have developed computer-aided system for automatic polyp segmentation to provide early clues of colorectal cancers.  ... 
doi:10.1109/access.2019.2900672 fatcat:o4zetkpi2rgsjh4bbubix447oe

Front Matter: Volume 9785

2016 Medical Imaging 2016: Computer-Aided Diagnosis  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  .  The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters.  ...  for characterization of prostate cancer: in vivo feasibility study [9785-55] 9785 1L An integrated classifier for computer-aided diagnosis of colorectal polyps based on random forest and location index  ... 
doi:10.1117/12.2240961 dblp:conf/micad/X16 fatcat:b5addnksdrgp3ixwvbjt53xeqe

Contour-aware Polyp Segmentation in Colonoscopy Images using Detailed Upsamling Encoder-Decoder Networks

Ngoc-Quang Nguyen, Duc My Vo, Sang-Woong Lee
2020 IEEE Access  
Moreover, we also present a complementary strategy for improving the method's segmentation performance based on a combination of a boundary-aware data augmentation method and an effective weighted loss  ...  Thus, this study was primarily motivated by the need to obtain an early and accurate diagnosis of polyps detected in colonoscopy images.  ...  [52] developed a new algorithm to extract oriented patches based on edge maps and classify them into polyp and non-polyp classes using a two-stage random forest classifier.  ... 
doi:10.1109/access.2020.2995630 fatcat:xjkuu7pjlvabhd3uqscss2zcza

Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer

Hang Qiu, Shuhan Ding, Jianbo Liu, Liya Wang, Xiaodong Wang
2022 Current Oncology  
Colorectal cancer (CRC) is one of the most common cancers worldwide.  ...  Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients' survival rate.  ...  tomography; MRI, magnetic resonance imaging; Faster R-CNN, faster region-based CNN; CAD, computer aided diagnosis).  ... 
doi:10.3390/curroncol29030146 pmid:35323346 pmcid:PMC8947571 fatcat:sw2bo7x225egrj5yznjprfsycy

A Holistic Multimedia System for Gastrointestinal Tract Disease Detection

Konstantin Pogorelov, Michael Riegler, Pål Halvorsen, Sigrun Losada Eskeland, Thomas de Lange, Carsten Griwodz, Kristin Ranheim Randel, Håkon Kvale Stensland, Duc-Tien Dang-Nguyen, Concetto Spampinato, Dag Johansen
2017 Proceedings of the 8th ACM on Multimedia Systems Conference - MMSys'17  
Analysis of medical videos for detection of abnormalities and diseases requires both high precision and recall, but also real-time processing for live feedback and scalability for massive screening of  ...  Existing work on this field does not provide the necessary combination of retrieval accuracy and performance.  ...  Secondly, it can be used as a computer-aided diagnosis system for medical experts.  ... 
doi:10.1145/3083187.3083189 dblp:conf/mmsys/PogorelovELGRSD17 fatcat:b7kielhvufgytpiv4vu6ou7jaa

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

John Gubatan, Steven Levitte, Akshar Patel, Tatiana Balabanis, Mike T Wei, Sidhartha R Sinha
2021 World Journal of Gastroenterology  
The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and  ...  , and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice.  ...  biopsy samples Diagnosis of IBD For the diagnosis of IBD, highest AUC attained by top Random Forest classifiers was 0.77.  ... 
doi:10.3748/wjg.v27.i17.1920 pmid:34007130 pmcid:PMC8108036 fatcat:rpyacvmeyfc6lnolasqb35zsee

Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy

Pablo Mesejo, Daniel Pizarro, Armand Abergel, Olivier Rouquette, Sylvain Beorchia, Laurent Poincloux, Adrien Bartoli
2016 IEEE Transactions on Medical Imaging  
Our technique also discriminates the severity of individual lesions in patients with many polyps, so that the gastroenterologist can directly focus on those requiring polypectomy.  ...  Serrated adenomas are very difficult to classify due to their mixed/hybrid nature and recent studies indicate that they can lead to colorectal cancer through the alternate serrated pathway.  ...  Two different ECs have been used and compared in this paper: i) Random Forests (RF) [76] and ii) Random Subspaces (RS) [77] .  ... 
doi:10.1109/tmi.2016.2547947 pmid:28005009 fatcat:7f3o4ocwurc77j6p2r4sntkoou

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Zi-Hang Chen, Li Lin, Chen-Fei Wu, Chao-Feng Li, Rui-Hua Xu, Ying Sun
2021 Cancer Communications  
On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer  ...  In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges.  ...  From a technical and practical aspect, these DL-based diagnostic tools integrate features for fine-tuning and enhancing performance, which simplifies the pipelines of conventional computer-aided diagnosis  ... 
doi:10.1002/cac2.12215 pmid:34613667 pmcid:PMC8626610 fatcat:nt5z4icazfarhhensa7lyoy2e4

LIRE

Mathias Lux, Michael Riegler, Pål Halvorsen, Konstantin Pogorelov, Nektarios Anagnostopoulos
2016 Proceedings of the 7th International Conference on Multimedia Systems - MMSys '16  
Then, we designed and developed a set of lesion and findings detection and localization approaches based on hand-crafted methods as well as on global-, local-and deepfeature-based methods, which serves  ...  Together with the traditional computer vision and medical imaging, core competencies of the multimedia community such as integration and analysis of data from several sources, real-time processing and  ...  , and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1145/2910017.2910630 dblp:conf/mmsys/LuxRHPA16 fatcat:jfweprlxfzb2lommgkw6nqqwi4

Texture Feature Extraction and Analysis for Polyp Differentiation via Computed Tomography Colonography

Yifan Hu, Zhengrong Liang, Bowen Song, Hao Han, Perry J. Pickhardt, Wei Zhu, Chaijie Duan, Hao Zhang, Matthew A. Barish, Chris E. Lascarides
2016 IEEE Transactions on Medical Imaging  
Image textures in computed tomography colonography (CTC) have great potential for differentiating non-neoplastic from neoplastic polyps and thus can advance the current CTC  ...  Acknowledgments This work was partly supported by the NIH/NCI under grants #CA082402 and #CA143111.  ...  Index Terms Colorectal polyps; computed tomography colonography; texture feature; polyp subtype classification I.  ... 
doi:10.1109/tmi.2016.2518958 pmid:26800530 pmcid:PMC4891231 fatcat:7ly24tq7vbairoeq27dxtkrera

Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons

Gian Eugenio Tontini, Alessandro Rimondi, Marta Vernero, Helmut Neumann, Maurizio Vecchi, Cristina Bezzio, Flaminia Cavallaro
2021 Therapeutic Advances in Gastroenterology  
Eleven records dealt with UC, five with CD and two with both. Most of the AI systems involved convolutional neural network, random forest and deep neural network architecture.  ...  Most studies focused on capsule endoscopy readings in CD ( n = 5) and on the AI-assisted assessment of mucosal activity in UC ( n = 10) for automated endoscopic scoring or real-time prediction of histological  ...  For this purpose, RF and CNN were employed to create an algorithm capable of automatically classifying UC, CD and ITB based on endoscopic results on a form or in free text.  ... 
doi:10.1177/17562848211017730 pmid:34178115 pmcid:PMC8202249 fatcat:nxtgnmhrozfsdaoghnsklpnypy

Artificial intelligence in gastroenterology: A state-of-the-art review

Paul T Kröner, Megan ML Engels, Benjamin S Glicksberg, Kipp W Johnson, Obaie Mzaik, Jeanin E van Hooft, Michael B Wallace, Hashem B El-Serag, Chayakrit Krittanawong
2021 World Journal of Gastroenterology  
The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception.  ...  , pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring  ...  ACKNOWLEDGEMENTS The authors are grateful to for Wang Z, PhD, Zhang HJ, PhD, Sun T, MD, PhD, Hassan Virk H, MD, and Aiumtrakul N, MD assistance with the additional literature search.  ... 
doi:10.3748/wjg.v27.i40.6794 pmid:34790008 pmcid:PMC8567482 fatcat:bd33eiilzbaitfzvcwwfg64wny

A Survey on Contemporary Computer-Aided Tumor, Polyp, and Ulcer Detection Methods in Wireless Capsule Endoscopy Imaging [article]

Tariq Rahim, Muhammad Arslan Usman, Soo Young Shin
2019 arXiv   pre-print
It can be hectic for a physician to go through such a large number of frames, hence computer-aided detection methods are considered an efficient alternative.  ...  Several studies have been included with in-depth detail of their methodologies, findings, and conclusions. Also, we have attempted to classify these methods based on their technical aspects.  ...  Random Tree (RT), Random Forest (RF), and Logistic Model Tree (LMT) classifiers are used having different combinations of color and texture features as an input vector.  ... 
arXiv:1910.00265v1 fatcat:cziq6sauuzaqpgpmca5pmgdwke

Deep Learning in Selected Cancers' Image Analysis—A Survey

Taye Girma Debelee, Samuel Rahimeto Kebede, Friedhelm Schwenker, Zemene Matewos Shewarega
2020 Journal of Imaging  
Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor.  ...  In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed.  ...  [56] proposed a CNN-based pre-trained model, VGGNet-16 for feature extraction, and use different classifiers namely: logistic regression, random forests, AdaBoost and SVM for classification of the pap-test  ... 
doi:10.3390/jimaging6110121 pmid:34460565 fatcat:2xvx5uya25a23nxicq3hdl42hi

Machine Learning for Endometrial Cancer Prediction and Prognostication

Vipul Bhardwaj, Arundhiti Sharma, Snijesh Valiya Parambath, Ijaz Gul, Xi Zhang, Peter E. Lobie, Peiwu Qin, Vijay Pandey
2022 Frontiers in Oncology  
The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer  ...  In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection,  ...  ML covers a variety of algorithms and statistical methods, including logistic regression, random forest, and DL-based approaches.  ... 
doi:10.3389/fonc.2022.852746 pmid:35965548 pmcid:PMC9365068 fatcat:t5apezcozrdodes4tyzhutxmii
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