Selective feature aggregation network with area-boundary constraints for polyp segmentation
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員

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摘要Automatic polyp segmentation is considered indispensable in modern polyp screening systems. It can help the clinicians accurately locate polyp areas for further diagnosis or surgeries. Benefit from the advancement of deep learning techniques, various neural networks are developed for handling the polyp segmentation problem. However, most of these methods neither aggregate multi-scale or multi-receptive-field features nor consider the area-boundary constraints. To address these issues, we propose a novel selective feature aggregation network with the area and boundary constraints. The network contains a shared encoder and two mutually constrained decoders for predicting polyp areas and boundaries, respectively. Feature aggregation is achieved by (1) introducing three up-concatenations between encoder and decoders and (2) embedding Selective Kernel Modules into convolutional layers which can adaptively extract features from different size of kernels. We call these two operations the Selective Feature Aggregation. Furthermore, a new boundary-sensitive loss function is proposed to take into account the dependency between the area and boundary branch, thus two branches can be reciprocally influenced and enable more accurate area predictions. We evaluate our method on the EndoScene dataset and achieve the state-of-the-art results with a Dice of 83.08% and a Accuracy of 96.68%.
出版社接受日期10.10.2019
著者Y Fang, C Chen, Y Yuan, K Tong
會議名稱22nd International Conference on Medical Image Computing and Computer-Assisted Intervention
會議開始日13.10.2019
會議完結日17.10.2019
會議地點China
會議國家/地區中國
會議論文集題名Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
系列標題Lecture Notes in Computer Science
出版年份2019
卷號11764
頁次302 - 310
國際標準書號978-3-030-32238-0
電子國際標準書號978-3-030-32239-7
語言美式英語
關鍵詞Automatic polyp segmentation, accuracy

上次更新時間 2021-23-02 於 00:16