Wan Jingjing, Chen Bolun, Yu Yongtao
Department of Gastroenterology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huaian 223002, China.
Department of Computer Science, Huaiyin Institute of Technology, Huaiyin 223001, China.
Diagnostics (Basel). 2021 Dec 3;11(12):2264. doi: 10.3390/diagnostics11122264.
High-quality colonoscopy is essential to prevent the occurrence of colorectal cancers. The data of colonoscopy are mainly stored in the form of images. Therefore, artificial intelligence-assisted colonoscopy based on medical images is not only a research hotspot, but also one of the effective auxiliary means to improve the detection rate of adenomas. This research has become the focus of medical institutions and scientific research departments and has important clinical and scientific research value.
In this paper, we propose a YOLOv5 model based on a self-attention mechanism for polyp target detection. This method uses the idea of regression, using the entire image as the input of the network and directly returning the target frame of this position in multiple positions of the image. In the feature extraction process, an attention mechanism is added to enhance the contribution of information-rich feature channels and weaken the interference of useless channels; Results: The experimental results show that the method can accurately identify polyp images, especially for the small polyps and the polyps with inconspicuous contrasts, and the detection speed is greatly improved compared with the comparison algorithm.
This study will be of great help in reducing the missed diagnosis of clinicians during endoscopy and treatment, and it is also of great significance to the development of clinicians' clinical work.
高质量的结肠镜检查对于预防结直肠癌的发生至关重要。结肠镜检查的数据主要以图像形式存储。因此，基于医学图像的人工智能辅助结肠镜检查不仅是研究热点，也是提高腺瘤检出率的有效辅助手段之一。该研究已成为医疗机构和科研部门的关注焦点，具有重要的临床和科研价值。
本文提出一种基于自注意力机制的YOLOv5模型用于息肉目标检测。该方法采用回归思想，将整幅图像作为网络输入，直接在图像的多个位置返回该位置的目标框。在特征提取过程中，添加注意力机制以增强信息丰富的特征通道的贡献，削弱无用通道的干扰。
实验结果表明，该方法能够准确识别息肉图像，尤其对于小息肉和对比度不明显的息肉，与对比算法相比，检测速度有了大幅提高。
本研究将对减少临床医生在内镜检查和治疗过程中的漏诊有很大帮助，对临床医生的临床工作开展也具有重要意义。
