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Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems

Country : Singapore
Department : Singapore Management University
Project Title : Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems
Researcher : AHN, Jungmo , HUYNH, Nguyen Loc , BALAN, Rajesh Krishna , LEE, Youngki , KO, JeongGil
Keyword : Medical Sciences , Software Engineering
Publisher : Institutional Knowledge at Singapore Management University
Year End : 2018
Identifier : https://ink.library.smu.edu.sg/sis_research/4053 , https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5056&context=sis_research
Source : Research Collection School Of Information Systems
Abstract / Description :

Capsule endoscopy identifies damaged areas in a patient's small intestine but often outputs poor-quality images or misses lesions, leading to either misdiagnosis or repetition of the lengthy procedure. The authors propose applying deep-learning models to automatically process the captured images and identify lesions in real time, enabling the capsule to take additional images of a specific location, adjust its focus level, or improve image quality. The authors also describe the technical challenges in realizing a viable automated capsule-endoscopy system.

References

AHN, Jungmo and others / et al. (2018). Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems.  Singapore: Singapore Management University.
AHN, Jungmo and others / et al. 2018. "Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems".  Singapore: Singapore Management University.
AHN, Jungmo and others / et al. "Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems."  Singapore: Singapore Management University, 2018. Print.
AHN, Jungmo and others / et al. Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems. Singapore: Singapore Management University; 2018.

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