用于息肉分割的多核位置嵌入ConvNeXt
Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation
- University of Science, VNU-HCM（胡志明市国家大学科学大学）
- Vietnam National University, Ho Chi Minh City（胡志明市越南国家大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对UNet跳跃连接存在的上下文信息不足与语义鸿沟问题，提出融合ConvNeXt骨干和多核位置嵌入模块的息肉分割框架，在Kvasir-SEG等数据集上取得了具有竞争力的分割精度。
AI中文摘要：
医学图像分割是辅助医生观察并做出精准诊断的技术，在结直肠癌领域尤为重要。具体而言，随着病例数增加，需要为大量患者提供更快更准确的诊断与识别；在内窥镜图像中，分割任务对帮助医生正确识别息肉位置或消化系统内的病变至关重要。因此，学界已开展大量工作将深度学习应用于息肉分割自动化，其中多数研究旨在改进U型结构。然而，UNet中简单的跳跃连接机制会导致上下文信息不足，且编码器与解码器输出的特征图之间存在语义鸿沟。为解决该问题，我们提出一种由ConvNeXt骨干网络与多核位置嵌入模块组成的新型框架。得益于所提出的模块，我们的方法能够在息肉分割任务中取得更优的准确率与泛化能力。大量实验表明，我们的模型在Kvasir-SEG数据集上达到了0.8818的Dice系数与0.8163的IOU得分。此外，在多个数据集上，我们的方法与此前的其他先进方法相比取得了具有竞争力的结果。
英文摘要：
Medical image segmentation is the technique that helps doctor view and has a precise diagnosis, particularly in Colorectal Cancer. Specifically, with the increase in cases, the diagnosis and identification need to be faster and more accurate for many patients; in endoscopic images, the segmentation task has been vital to helping the doctor identify the position of the polyps or the ache in the system correctly. As a result, many efforts have been made to apply deep learning to automate polyp segmentation, mostly to ameliorate the U-shape structure. However, the simple skip connection scheme in UNet leads to deficient context information and the semantic gap between feature maps from the encoder and decoder. To deal with this problem, we propose a novel framework composed of ConvNeXt backbone and Multi Kernel Positional Embedding block. Thanks to the suggested module, our method can attain better accuracy and generalization in the polyps segmentation task. Extensive experiments show that our model achieves the Dice coefficient of 0.8818 and the IOU score of 0.8163 on the Kvasir-SEG dataset. Furthermore, on various datasets, we make competitive achievement results with other previous state-of-the-art methods.
