| Title |
Bi-directional Fusion Network to Reduce Semantic Gaps between Multi-scale Polyp Feature Maps |
| Authors |
김수정(Su Jung Kim) ; 남주현(Ju Hyeon Nam) ; 이상철(Sang-chul Lee) |
| DOI |
https://doi.org/10.5573/ieie.2023.60.11.47 |
| Keywords |
Deep learning; Convolutional neural network; Polyp segmentation; Colonoscopy; Colorectal cancer |
| Abstract |
Colonoscopy is the gold standard method for detecting colorectal polyps that are likely to progress into colorectal cancer. Many deep learning models have been suggested to aid and assist doctors in detecting and removing potentially harmful polyps during colonoscopy. However, segmenting polyp images can be a challenging task due to polyps’ variation in size, shape, and color and their similarity to the surrounding mucosal area. In order to effectively segment polyps, it is essential to fuse feature maps of various sizes while considering the semantic gaps in between. To tackle this problem, we propose MPISNet (Multi-scale Polyp Image Segemtnation Network) which is designed to detect polyps that vary in size by filling in the semantic gaps between features produced with different receptive fields. Specifically, a novel SCBi-FPN module was used to effectively fuse features of different sizes and CBAM module to re-calibrate feature maps using both channel and spatial attentions. Our network outperforms PraNet, a previous network for polyp segmentation, in Kvasir-SEG dataset by 1.72%, 1.15%, 2.13%, in F1-score, Recall, and mIoU, while showing a reasonable mean inference rate of 180fps. |