PEFNet: Positional Embedding Feature for Polyp Segmentation

Trong-Hieu Nguyen Mau, Quoc-Huy Trinh, Nhat-Tan Bui, Phuoc-Thao Vo Thi, Minh-Van Nguyen, Xuan-Nam Cao, Minh-Triet Tran, Hai-Dang Nguyen

Abstract: With the development of biomedical computing, the segmentation task is integral in helping the doctor correctly identify the position of the polyps or the ache in the system. However, precise polyp segmentation is challenging because the same type of polyps has a diversity of size, color, and texture; previous methods cannot fully transfer information from encoder to decoder due to the lack of details and knowledge of previous layers. To deal with this problem, we propose PEFNet, a novel model using modified UNet with a new Positional Embedding Feature block in the merging stage, which has more accuracy and generalization in polyps segmentation. The PEF block utilizes the information of the position, concatenated features, and extracted features to enrich the gained knowledge and improve the model’s comprehension ability. With EfficientNetV2-L as the backbone, we obtain the IOU score of 0.8201 and the Dice coefficient of 0.8802 on the Kvasir-SEG dataset. By PEFNet, we also take second place on the task Medico: Transparency in Medical Image Segmentation at MediaEval 2021, which is clear proof of the effectiveness of our models.