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).

SPEEDNet 是一种用于结肠镜图像中精确定位和分割病变的新型网络架构，通过将扩张卷积和缩减层成对组合成金字塔结构来降低参数数量，提高特征学习，并降低模糊效果。在 EBHISeg 数据集上，SPEEDNet 的性能优于 UNet、FeedNet 和 AttesResDUNet，达到了 0.952 的平均 Dice 得分和 0.971 的召回率。与 UNet、FeedNet 和 AttesResDUNet 模型相比，SPEEDNet 的模型大小为 9.81 MB，显著较小。