SPEEDNet：用于结肠镜图像的显著金字塔增强编码器-解码器网络
SPEEDNet: Salient Pyramidal Enhancement Encoder-Decoder Network for Colonoscopy Images
AI总结：
SPEEDNet提出DIPC金字塔卷积融合模块，在结肠镜图像病变分割中以9.81 MB小模型取得0.952 Dice和0.971召回率，优于UNet等网络。
摘要：
准确识别并精确勾画肿瘤或病变等重要区域是医学影像分析的一个关键目标。本文提出SPEEDNet，一种用于在结肠镜图像中精确分割病变的新型架构。SPEEDNet使用一种名为扩张-对合金字塔卷积融合（DIPC）的新型模块。DIPC模块将扩张对合层成对组合成金字塔结构，以将特征图转换到紧凑空间。这降低了参数总量，同时在最优感受野上改善表示学习，从而减少模糊效应。在EBHISeg数据集上，SPEEDNet优于三个先前网络：UNet、FeedNet和AttesResDUNet。具体而言，SPEEDNet达到0.952的平均Dice分数和0.971的召回率。定性结果和消融研究进一步揭示了SPEEDNet的有效性。SPEEDNet的模型大小为9.81 MB，显著小于UNet（22.84 MB）、FeedNet（185.58 MB）和AttesResDUNet（140.09 MB）。
英文摘要：
Accurate identification and precise delineation of regions of significance, such as tumors or lesions, is a pivotal goal in medical imaging analysis. This paper proposes SPEEDNet, a novel architecture for precisely segmenting lesions within colonoscopy images. SPEEDNet uses a novel block named Dilated-Involutional Pyramidal Convolution Fusion (DIPC). A DIPC block combines the dilated involution layers pairwise into a pyramidal structure to convert the feature maps into a compact space. This lowers the total number of parameters while improving the learning of representations across an optimal receptive field, thereby reducing the blurring effect. On the EBHISeg dataset, SPEEDNet outperforms three previous networks: UNet, FeedNet, and AttesResDUNet. Specifically, SPEEDNet attains an average dice score of 0.952 and a recall of 0.971. Qualitative results and ablation studies provide additional insights into the effectiveness of SPEEDNet. The model size of SPEEDNet is 9.81 MB, significantly smaller than that of UNet (22.84 MB), FeedNet(185.58 MB), and AttesResDUNet (140.09 MB).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
