@inproceedings {27858,
	title = {Progressively Normalized Self-Attention Network for Video Polyp Segmentation},
	journal = {Medical Image Computing and Computer Assisted Intervention (MICCAI 2021)},
	volume = {LNCS, volume 12901},
	year = {2021},
	pages = {142-152},
	publisher = { Springer},
	abstract = {Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features. However, due to their limited receptive fields, CNNs can not fully exploit the global temporal and spatial information in successive video frames, resulting in false positive segmentation results. In this paper, we propose the novel PNS-Net(Progressively Normalized Self-attention Net-work), which can efficiently learn representations from polyp videos with real-time speed (\~{}140fps) on a single RTX 2080 GPU and no post-processing. OurPNS-Netis based solely on a basic normalized self-attention block, equipping with recurrence and CNNs entirely. Experiments on challenging VPS datasets demonstrate that the proposed PMS-Netachieves state-of-the-art performance. We also conduct extensive experiments to study the effectiveness of the channel split, soft-attention, and progressive learning strategy. We find that our PNS-Networks well under different settings, making it a promising solution to the VPS task.},
	keywords = {colonoscopy, Normalized self-attention, Polyp segmentation},
	author = {Ji, Ge-Peng and Chou, Yu-Cheng and Fan, Deng-Ping and Chen, Geng and Fu, Huazhu and Jha, Debesh and Shao, Ling}
}
