Abstract
For polyp detection, we adapt a Faster R-CNN (Ren et al. 2015) architecture shown in Fig. 11.1. Faster R-CNN has two stages: region proposal network (RPN), and a box classifier network. Both stages share a common set of convolutional layers as a feature extractor to reduce the marginal cost for detection. The RPN utilizes feature maps of the last convolutional layer to generate class-agnostic RoI proposals called anchors, each with an objectness confidence value. The anchors have different aspect ratios and scales. The classifier network crops these anchors from the feature maps of the last convolutional layer and feeds the cropped features to the remainder of the network in order to predict location and confidence values of the object class (polyps).
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Qadir, H.A., Balasingham, I., Shin, Y. (2021). Region-Based Convolutional Neural Network for Polyp Detection and Segmentation. In: Bernal, J., Histace, A. (eds) Computer-Aided Analysis of Gastrointestinal Videos. Springer, Cham. https://doi.org/10.1007/978-3-030-64340-9_11
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DOI: https://doi.org/10.1007/978-3-030-64340-9_11
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