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Distilling the knowledge in CNN for WCE screening tool

Authors :
Xavier Dray
Orlando Chuquimia
Hichem Sahbi
Andrea Pinna
Bertrand Granado
Thomas Garbay
Systèmes Electroniques (SYEL)
LIP6
Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Machine Learning and Information Access (MLIA)
Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)
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

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