JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Class-Labeling Method for Designing a Deep Neural Network of Capsule Endoscopic Images Using a Lesion-Focused Knowledge Model |
| 영문제목(English Title) |
Class-Labeling Method for Designing a Deep Neural Network of Capsule Endoscopic Images Using a Lesion-Focused Knowledge Model |
| 저자(Author) |
Ye-Seul Park
Jung-Won Lee
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| 원문수록처(Citation) |
VOL 16 NO. 01 PP. 0171 ~ 0183 (2020. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Capsule endoscopy is one of the increasingly demanded diagnostic methods among patients in recent years because of its ability to observe small intestine difficulties. It is often conducted for 12 to 14 hours, but significant frames constitute only 10% of whole frames. Thus, it has been designed to automatically acquire significant frames through deep learning. For example, studies to track the position of the capsule (stomach, small intestine, etc.) or to extract lesion-related information (polyps, etc.) have been conducted. However, although grouping or labeling the training images according to similar features can improve the performance of a learning model, various attributes (such as degree of wrinkles, presence of valves, etc.) are not considered in conventional approaches. Therefore, we propose a class-labeling method that can be used to design a learning model by constructing a knowledge model focused on main lesions defined in standard terminologies for capsule endoscopy (minimal standard terminology, capsule endoscopy structured terminology). This method enables the designing of a systematic learning model by labeling detailed classes through differentiation of similar characteristics.
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| 키워드(Keyword) |
Capsule Endoscopy
Class-labeling Method
Deep Learning
Knowledge Base (KB)
Ontology
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| 파일첨부 |
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