Abstract
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.
| Original language | English |
|---|---|
| Pages | 68-76 |
| Number of pages | 9 |
| Volume | 51 |
| No. | 5 |
| Specialist publication | Computer |
| DOIs | |
| Publication status | Published - 2018 May |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
Cite this
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Finding Small-Bowel Lesions : Challenges in Endoscopy-Image-Based Learning Systems. / Ahn, Jungmo; Nguyen Loc, Huynh; Krishna Balan, Rajesh; Lee, Youngki; Ko, Jeonggil.
In: Computer, Vol. 51, No. 5, 05.2018, p. 68-76.Research output: Contribution to specialist publication › Article
TY - GEN
T1 - Finding Small-Bowel Lesions
T2 - Challenges in Endoscopy-Image-Based Learning Systems
AU - Ahn, Jungmo
AU - Nguyen Loc, Huynh
AU - Krishna Balan, Rajesh
AU - Lee, Youngki
AU - Ko, Jeonggil
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85047769070&partnerID=8YFLogxK
U2 - 10.1109/MC.2018.2381116
DO - 10.1109/MC.2018.2381116
M3 - Article
AN - SCOPUS:85047769070
VL - 51
SP - 68
EP - 76
JO - ACM SIGPLAN/SIGSOFT Workshop on Program Analysis for Software Tools and Engineering
JF - ACM SIGPLAN/SIGSOFT Workshop on Program Analysis for Software Tools and Engineering
SN - 0018-9162
ER -