提出多尺度多路径级联网络，提升结直肠息肉分割精度。
Multi-scale and Multi-path Cascaded Convolutional Network for Semantic Segmentation of Colorectal Polyps
- 采用多尺度多路径级联卷积与双注意力模块融合特征。
- 在6个数据集上达到94.71%的Dice分数和90.53%的MIoU。
- 适合医学图像分割研究者及临床辅助诊断系统开发。
结直肠息肉是消化道结构异常，部分可能恶变。本文提出一种新型息肉分割框架MMCC-Net，旨在解决现有模型对空间依赖性建模不足、解码阶段缺乏多层级特征融合的问题。通过引入多尺度多路径级联卷积技术，并结合双注意力模块、跳跃连接与特征增强器，强化特征聚合能力。在六个公开数据集上测试，并与八种先进模型对比，结果表明：在六组数据中，其Dice分数置信区间为(77.08, 77.56)至(94.19, 94.71)，平均交并比（MIoU）置信区间为(72.20, 73.00)至(89.69, 90.53)。该表现证明了模型在像素级息肉识别中的高效性与准确性，有助于结直肠癌早期筛查与预防策略的实现。
原文摘要 · Abstract (English)
Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence intervals ranging between (77.08, 77.56) and (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence intervals ranging from (72.20, 73.00) to (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer.
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