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Distilling the knowledge in CNN for WCE screening tool
- Source :
- 2019 Conference on Design and Architectures for Signal and Image Processing (DASIP), 2019 Conference on Design and Architectures for Signal and Image Processing (DASIP), Oct 2019, Montreal, Canada. pp.19-22, ⟨10.1109/DASIP48288.2019.9049201⟩, DASIP
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- International audience; A way to improve the early detection of colorectal cancer is screening. Polyps are a marker of colorectal cancer and the best modality to detect them is the image. In 2003 Wireless Capsule Endoscopy was introduced and opened a way to integrate automatic image processing to realize a screening tool. Moreover, the capacity to detect polyp with Convolutional Neural Network was shown in many scientific studies, but one issue is the integration of these networks. In this article, we present our works to integrate CNN or image processing based on a CNN inside a WCE to realize a powerful screening tool. We apply the knowledge distillation method. We prove that knowledge distillation is efficient from VGG16 to Squeezenet in polyp detection context
- Subjects :
- Modality (human–computer interaction)
business.industry
Computer science
0206 medical engineering
Early detection
Image processing
Context (language use)
02 engineering and technology
Machine learning
computer.software_genre
020601 biomedical engineering
Convolutional neural network
Distillation method
3. Good health
03 medical and health sciences
0302 clinical medicine
030220 oncology & carcinogenesis
Screening tool
[INFO]Computer Science [cs]
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- 2019 Conference on Design and Architectures for Signal and Image Processing (DASIP), 2019 Conference on Design and Architectures for Signal and Image Processing (DASIP), Oct 2019, Montreal, Canada. pp.19-22, ⟨10.1109/DASIP48288.2019.9049201⟩, DASIP
- Accession number :
- edsair.doi.dedup.....804d0d968534e0cd4be47f1869ef4361