Li Chenqian, Liu Jun, Tang Jinshan
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China.
Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan 430065, China.
Math Biosci Eng. 2024 Jan 8;21(2):2024-2049. doi: 10.3934/mbe.2024090.
Accurate classification and segmentation of polyps are two important tasks in the diagnosis and treatment of colorectal cancers. Existing models perform segmentation and classification separately and do not fully make use of the correlation between the two tasks. Furthermore, polyps exhibit random regions and varying shapes and sizes, and they often share similar boundaries and backgrounds. However, existing models fail to consider these factors and thus are not robust because of their inherent limitations. To address these issues, we developed a multi-task network that performs both segmentation and classification simultaneously and can cope with the aforementioned factors effectively. Our proposed network possesses a dual-branch structure, comprising a transformer branch and a convolutional neural network (CNN) branch. This approach enhances local details within the global representation, improving both local feature awareness and global contextual understanding, thus contributing to the improved preservation of polyp-related information. Additionally, we have designed a feature interaction module (FIM) aimed at bridging the semantic gap between the two branches and facilitating the integration of diverse semantic information from both branches. This integration enables the full capture of global context information and local details related to polyps. To prevent the loss of edge detail information crucial for polyp identification, we have introduced a reverse attention boundary enhancement (RABE) module to gradually enhance edge structures and detailed information within polyp regions. Finally, we conducted extensive experiments on five publicly available datasets to evaluate the performance of our method in both polyp segmentation and classification tasks. The experimental results confirm that our proposed method outperforms other state-of-the-art methods.
息肉的准确分类和分割是结直肠癌诊断和治疗中的两项重要任务。现有模型分别进行分割和分类，没有充分利用这两项任务之间的相关性。此外，息肉呈现出随机的区域以及不同的形状和大小，并且它们常常具有相似的边界和背景。然而，现有模型未能考虑这些因素，因此由于其固有的局限性而不够稳健。为了解决这些问题，我们开发了一种多任务网络，它能同时执行分割和分类，并能有效应对上述因素。我们提出的网络具有双分支结构，包括一个Transformer分支和一个卷积神经网络（CNN）分支。这种方法增强了全局表示中的局部细节，提高了局部特征感知和全局上下文理解能力，从而有助于更好地保留与息肉相关的信息。此外，我们设计了一个特征交互模块（FIM），旨在弥合两个分支之间的语义差距，并促进来自两个分支的不同语义信息的整合。这种整合能够全面捕捉与息肉相关的全局上下文信息和局部细节。为了防止对息肉识别至关重要的边缘细节信息丢失，我们引入了反向注意力边界增强（RABE）模块，以逐步增强息肉区域内的边缘结构和详细信息。最后，我们在五个公开可用的数据集上进行了广泛的实验，以评估我们的方法在息肉分割和分类任务中的性能。实验结果证实，我们提出的方法优于其他现有最先进的方法。
