Poster + Paper
3 April 2023 Dynamic attention deconvolutional single shot detector for polyp detection and classification in narrow-band imaging
Author Affiliations +
Conference Poster
Abstract
To deal with multitask segmentation, detection and classification of colon polyps, and solve the clinical problems of small polyps with similar background, missed detection and difficult classification, we have realized the method of supporting the early diagnosis and correct treatment of gastrointestinal endoscopy on the computer. We apply the residual U-structure network with image processing to segment polyps, and a Dynamic Attention Deconvolutional Single Shot Detector (DAD-SSD) to classify various polyps on colonic narrow-band images. The residual U-structure network is a two-level nested U-structure that is able to capture more contextual information, and the image processing improves the segmentation problem. DAD-SSD consists of Attention Deconvolutional Module (ADM) and Dynamic Convolutional Prediction Module (DCPM) to extract and fuse context features. We evaluated narrow-band images, and the experimental results validate the effectiveness of the method in dealing with such multi-task detection and classification. Particularly, the mean average precision (mAP) and accuracy are superior to other methods in our experiment, which are 76.55% and 74.4% respectively.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Guanping Xu, Yu Wu, Bei Li, Dongfang Shen, Ming Wu, Ao Wang, Wenkang Fan, Hong Shi, Jianhua Chen, Yinran Chen, and Xiongbiao Luo "Dynamic attention deconvolutional single shot detector for polyp detection and classification in narrow-band imaging", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 124662M (3 April 2023); https://doi.org/10.1117/12.2654083
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KEYWORDS
Polyps

Image segmentation

Object detection

Deconvolution

Image classification

Cancer detection

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