Abstract Prevention of colorectal cancer (CRC) by inspecting and removing colorectal polyps has become a global health priority because CRC is one of the most frequent cancers in the world. Although recent U-Net-based convolutional neural networks (CNNs) with deep feature representation and skip connections have shown to segment polyps effectively, U-Net-based approaches still have limitations in modeling explicit global contexts, due to the intrinsic nature locality of convolutional operations. To overcome these problems, this study proposes a novel deep learning model, SwinE-Net, for polyp segmentation that effectively combines a CNN-based EfficientNet and Vision Transformer (ViT)-based Swin Ttransformer. The main challenge is to conduct accurate and robust medical segmentation in maintaining global semantics without sacrificing low-level features of CNNs through Swin Transformer. First, the multidilation convolutional block generates refined feature maps to enhance feature discriminability for multilevel feature maps extracted from CNN and ViT. Then, the multifeature aggregation block creates intermediate side outputs from the refined polyp features for efficient training. Finally, the attentive deconvolutional network-based decoder upsamples the refined and combined feature maps to accurately segment colorectal polyps. We compared the proposed approach with previous state-of-the-art methods by evaluating various metrics using five public datasets (Kvasir, ClinicDB, ColonDB, ETIS, and EndoScene). The comparative evaluation, in particular, proved that the proposed approach showed much better performance in the unseen dataset, which shows the generalization and scalability in conducting polyp segmentation. Furthermore, an ablation study was performed to prove the novelty and advantage of the proposed network. The proposed approach outperformed previous studies.

中文翻译：

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SwinE-Net：使用卷积神经网络和 Swin Transformer 进行新型息肉分割的混合深度学习方法

摘要由于结直肠癌是世界上最常见的癌症之一，通过检查和切除结直肠息肉来预防结直肠癌 (CRC) 已成为全球卫生优先事项。尽管最近基于 U-Net 的具有深度特征表示和跳跃连接的卷积神经网络 (CNN) 已显示可以有效地分割息肉，但由于卷积的固有局部性，基于 U-Net 的方法在显式全局上下文建模方面仍然存在局限性操作。为了克服这些问题，本研究提出了一种用于息肉分割的新型深度学习模型 SwinE-Net，该模型有效地结合了基于 CNN 的 EfficientNet 和基于视觉转换器 (ViT) 的 Swin Ttransformer。主要挑战是通过 Swin Transformer 在不牺牲 CNN 的低级特征的情况下，在保持全局语义的情况下进行准确且稳健的医学分割。首先，多膨胀卷积块生成精细的特征图，以增强从 CNN 和 ViT 提取的多级特征图的特征可辨别性。然后，多特征聚合块从细化的息肉特征创建中间侧输出，以进行有效训练。最后，细心的基于反卷积网络的解码器对细化和组合的特征图进行上采样，以准确分割结直肠息肉。我们通过使用五个公共数据集（Kvasir、ClinicDB、ColonDB、ETIS 和 EndoScene）评估各种指标，将所提出的方法与之前最先进的方法进行了比较。比较评价，特别是，证明了所提出的方法在看不见的数据集中表现出更好的性能，这表明了进行息肉分割的泛化性和可扩展性。此外，进行了消融研究以证明所提出网络的新颖性和优势。所提出的方法优于以前的研究。