Polyp segmentation is crucial for preventing colorectal cancer a common type
of cancer. Deep learning has been used to segment polyps automatically, which
reduces the risk of misdiagnosis. Localizing small polyps in colonoscopy images
is challenging because of its complex characteristics, such as color,
occlusion, and various shapes of polyps. To address this challenge, a novel
frequency-based fully convolutional neural network, Multi-Frequency Feature
Fusion Polyp Segmentation Network (M3FPolypSegNet) was proposed to decompose
the input image into low/high/full-frequency components to use the
characteristics of each component. We used three independent multi-frequency
encoders to map multiple input images into a high-dimensional feature space. In
the Frequency-ASPP Scalable Attention Module (F-ASPP SAM), ASPP was applied
between each frequency component to preserve scale information. Subsequently,
scalable attention was applied to emphasize polyp regions in a high-dimensional
feature space. Finally, we designed three multi-task learning (i.e., region,
edge, and distance) in four decoder blocks to learn the structural
characteristics of the region. The proposed model outperformed various
segmentation models with performance gains of 6.92% and 7.52% on average for
all metrics on CVC-ClinicDB and BKAI-IGH-NeoPolyp, respectively.

通过使用多频特征融合息肉分割网络 (M3FPolypSegNet)，以及多任务学习的方法，本研究提出了一种新颖的基于频率的全卷积神经网络，能够自动地分割结肠镜图像中的小型息肉，提高了分割的准确性和效率。