Colorectal cancer (CRC) represents one of the common malignancies of the gastrointestinal tract. The CRC incidence and mortality rates can be significantly reduced through early detection and resection of the precursor lesions, also known the colorectal polyps. However, such polyps can be missed during manual colonoscopy screening. With recent advances in artificial intelligence, numerous computer-aided diagnosis (CAD) methods have been proposed for colonoscopy applications. In particular, deep learning algorithms have been recently designed to incorporate sophisticated attention mechanisms into convolutional blocks and hence demonstrate a great potential for enhancing the performance of convolutional neural networks (CNNs). Nevertheless, most current deep learning techniques suffer from the high model complexity and excessive computational burden. In this paper, we introduce a deep learning approach for colorectal polyp detection and segmentation. Specifically, we propose a new shuffle efficient channel attention network (sECANet) with no dimensionality reduction. This network can be exploited to learn effective channel attention by obtaining cross-channel interactions. A total of 2112 manually-labeled images were collected from 1197 patients in a local hospital using colonoscopy screening. Additional data samples were collected from the CVC-ClinicDB, the ETIS-Larib Polyp DB and the Kvasir-SEG dataset. The captured images were partitioned into 3590 training images and 330 testing images, and each image was labeled as a polyp or non-polyp image. We assessed our framework on the testing images and achieved a precision of 94.9%, a recall of 96.9%, a F1 score of 95.9%, and a F2 score of 96.5%. In conclusion, our proposed framework has a great potential of assisting endoscopists in tracking polyps during colonoscopy and therefore performing early and timely resection of such polyps before they evolve into invasive cancer types.

结直肠癌 (CRC) 是胃肠道常见的恶性肿瘤之一。通过早期发现和切除前体病变，也称为结直肠息肉，可以显着降低 CRC 的发病率和死亡率。然而，在手动结肠镜检查期间可能会遗漏此类息肉。随着人工智能的最新进展，已经提出了许多用于结肠镜检查应用的计算机辅助诊断 (CAD) 方法。特别是，最近设计的深度学习算法将复杂的注意力机制结合到卷积块中，因此显示出增强卷积神经网络 (CNN) 性能的巨大潜力。然而，目前大多数深度学习技术都存在模型复杂度高和计算负担过重的问题。在本文中，我们介绍了一种用于结直肠息肉检测和分割的深度学习方法。具体来说，我们提出了一种新的 shuffle 高效通道注意力网络 (sECANet)，没有降维。通过获得跨渠道交互，可以利用该网络来学习有效的渠道注意力。使用结肠镜筛查从当地医院的 1197 名患者中收集了总共 2112 张手动标记的图像。从 CVC-ClinicDB、ETIS-Larib Polyp DB 和 Kvasir-SEG 数据集收集了其他数据样本。将捕获的图像划分为 3590 张训练图像和 330 张测试图像，并将每个图像标记为息肉或非息肉图像。我们在测试图像上评估了我们的框架，并获得了 94.9% 的准确率、96.9% 的召回率、95.9% 的 F1 分数和 96.5% 的 F2 分数。