Abstract
A common limitation to almost all state-of-the-art techniques for automated polyp detection and delineation is that they are based on still-frame analysis (Wang et al. 2018; Urban et al. 2018; Shin et al. 2018; Mohammed et al. 2018). Colonoscopy, however, is a video-based modality and an endoscopist will always use the contextual information from previous frames to make an accurate decision about the potential presence of a polyp. Recent developments in semantic segmentation of videos in non-medical applications (Fayyaz et al. 2016; Valipour et al. 2017) show that including temporal features in a CNN can increase its performance and also yield more consistent results over time. Recurrent neural networks (RNNs) are a commonly used concept for sequence modeling and essentially allow neural networks to retain information over time. Long short-term memory (LSTM) models are a type of RNNs that can model longer time dependencies than traditional RNNs and it is a convolutional variant of these LSTM models (Xingjian et al. 2015) that is used in this chapter. The latter can not only encode temporal features but can simultaneously incorporate spatial features into one single layer.
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Eelbode, T., Sinonquel, P., Bisschops, R., Maes, F. (2021). Convolutional LSTM. In: Bernal, J., Histace, A. (eds) Computer-Aided Analysis of Gastrointestinal Videos. Springer, Cham. https://doi.org/10.1007/978-3-030-64340-9_14
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DOI: https://doi.org/10.1007/978-3-030-64340-9_14
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