双分支编码器提升肠息肉分割精度，性能超越主流模型。
DPE-Net: Dual-Parallel Encoder Based Network for Semantic Segmentation of Polyps
- 设计双并行编码器，融合双卷积与单卷积特征提取机制
- 在Kvasir和CVC-ClinicDB上分别达Dice 0.919/0.931，mIoU 0.866/0.891
- 适合医学图像分割研究者参考，尤其关注肠镜影像分析
在医学影像中，高效分割结肠息肉对实现结直肠癌的微创治疗至关重要。本文提出一种新型网络结构，采用双并行编码器分支进行息肉分割。一个分支使用具备深层特征保持能力的双卷积块，另一分支则结合单卷积块与前层特征，增强特征提取多样性；在转置层前通过深度可分离拼接操作融合两分支特征。该模型在Kvasir和CVC-ClinicDB数据集上表现优异，分别取得Dice分数0.919、mIoU 0.866，以及Dice分数0.931、mIoU 0.891。可视化与定量结果均表明模型有效性，有望成为医学图像分割的新基准。
原文摘要 · Abstract (English)
In medical imaging, efficient segmentation of colon polyps plays a pivotal role in minimally invasive solutions for colorectal cancer. This study introduces a novel approach employing two parallel encoder branches within a network for polyp segmentation. One branch of the encoder incorporates the dual convolution blocks that have the capability to maintain feature information over increased depths, and the other block embraces the single convolution block with the addition of the previous layer's feature, offering diversity in feature extraction within the encoder, combining them before transpose layers with a depth-wise concatenation operation. Our model demonstrated superior performance, surpassing several established deep-learning architectures on the Kvasir and CVC-ClinicDB datasets, achieved a Dice score of 0.919, a mIoU of 0.866 for the Kvasir dataset, and a Dice score of 0.931 and a mIoU of 0.891 for the CVC-ClinicDB. The visual and quantitative results highlight the efficacy of our model, potentially setting a new model in medical image segmentation.
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