Machine Learning-Based Colorectal Cancer Detection

Victoria  Blanes-Vidal; Gunnar Baatrup; Esmaeil S. Nadimi

doi:10.1145/3264746.3264785

Machine Learning-Based Colorectal Cancer Detection

Victoria Blanes-Vidal, Gunnar Baatrup, Esmaeil S. Nadimi

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populations. However, before it can be widely applied, significant research priorities need to be addressed. In this study, we present an innovative machine learning-based algorithm which can considerably improve acquisition and analysis of relevant data on colorectal polyps obtained from capsule endoscopy. The algorithm is to match CCE and colonoscopy polyps, based on objective measures of similarity between polyps. our matching algorithm is able to objectively quantify the similarity between CCE and colonoscopy polyps based on their size, morphology and location, and provides a one-to-one unequivocal match between CCE and colonoscopy polyps.

Original language	English
Title of host publication	Proceedings of the 2018 Research in Adaptive and Convergent Systems, RACS 2018 : RACS 2018
Publisher	Association for Computing Machinery
Publication date	9. Oct 2018
Pages	43-46
ISBN (Electronic)	978-1-4503-5885-9
DOIs	https://doi.org/10.1145/3264746.3264785
Publication status	Published - 9. Oct 2018
Event	Research in Adaptive and Convergent Systems - Honolulu, Hawaii, United States Duration: 9. Oct 2018 → 12. Oct 2018

Conference

Conference	Research in Adaptive and Convergent Systems
Country/Territory	United States
City	Honolulu, Hawaii
Period	09/10/2018 → 12/10/2018

Keywords

Capsule endoscopy
Colorectal cancer
Matching
Polyp

Documents & Links

10.1145/3264746.3264785

Cite this

@inproceedings{3cd5c9be0e1b47a9a2554e38b02335fb,

title = "Machine Learning-Based Colorectal Cancer Detection",

abstract = "Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populations. However, before it can be widely applied, significant research priorities need to be addressed. In this study, we present an innovative machine learning-based algorithm which can considerably improve acquisition and analysis of relevant data on colorectal polyps obtained from capsule endoscopy. The algorithm is to match CCE and colonoscopy polyps, based on objective measures of similarity between polyps. our matching algorithm is able to objectively quantify the similarity between CCE and colonoscopy polyps based on their size, morphology and location, and provides a one-to-one unequivocal match between CCE and colonoscopy polyps.",

keywords = "Capsule endoscopy, Colorectal cancer, Matching, Polyp",

author = "Victoria Blanes-Vidal and Gunnar Baatrup and {S. Nadimi}, Esmaeil",

year = "2018",

month = oct,

day = "9",

doi = "10.1145/3264746.3264785",

language = "English",

pages = "43--46",

booktitle = "Proceedings of the 2018 Research in Adaptive and Convergent Systems, RACS 2018",

publisher = "Association for Computing Machinery",

address = "United States",

note = "Research in Adaptive and Convergent Systems, RACS 2018 ; Conference date: 09-10-2018 Through 12-10-2018",

}

Blanes-Vidal, V , Baatrup, G & S. Nadimi, E 2018, Machine Learning-Based Colorectal Cancer Detection. in Proceedings of the 2018 Research in Adaptive and Convergent Systems, RACS 2018: RACS 2018. Association for Computing Machinery, pp. 43-46, Research in Adaptive and Convergent Systems, Honolulu, Hawaii, United States, 09/10/2018. https://doi.org/10.1145/3264746.3264785

Machine Learning-Based Colorectal Cancer Detection. / Blanes-Vidal, Victoria ; Baatrup, Gunnar ; S. Nadimi, Esmaeil.
Proceedings of the 2018 Research in Adaptive and Convergent Systems, RACS 2018: RACS 2018. Association for Computing Machinery, 2018. p. 43-46.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Machine Learning-Based Colorectal Cancer Detection

AU - Blanes-Vidal, Victoria

AU - Baatrup, Gunnar

AU - S. Nadimi, Esmaeil

PY - 2018/10/9

Y1 - 2018/10/9

N2 - Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populations. However, before it can be widely applied, significant research priorities need to be addressed. In this study, we present an innovative machine learning-based algorithm which can considerably improve acquisition and analysis of relevant data on colorectal polyps obtained from capsule endoscopy. The algorithm is to match CCE and colonoscopy polyps, based on objective measures of similarity between polyps. our matching algorithm is able to objectively quantify the similarity between CCE and colonoscopy polyps based on their size, morphology and location, and provides a one-to-one unequivocal match between CCE and colonoscopy polyps.

AB - Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populations. However, before it can be widely applied, significant research priorities need to be addressed. In this study, we present an innovative machine learning-based algorithm which can considerably improve acquisition and analysis of relevant data on colorectal polyps obtained from capsule endoscopy. The algorithm is to match CCE and colonoscopy polyps, based on objective measures of similarity between polyps. our matching algorithm is able to objectively quantify the similarity between CCE and colonoscopy polyps based on their size, morphology and location, and provides a one-to-one unequivocal match between CCE and colonoscopy polyps.

KW - Capsule endoscopy

KW - Colorectal cancer

KW - Matching

KW - Polyp

U2 - 10.1145/3264746.3264785

DO - 10.1145/3264746.3264785

M3 - Article in proceedings

SP - 43

EP - 46

BT - Proceedings of the 2018 Research in Adaptive and Convergent Systems, RACS 2018

PB - Association for Computing Machinery

T2 - Research in Adaptive and Convergent Systems

Y2 - 9 October 2018 through 12 October 2018

ER -

Machine Learning-Based Colorectal Cancer Detection

Abstract

Conference

Keywords

Documents & Links

Fingerprint

Cite this