Accurate and fast segmentation of medical images is clinically essential, yet current research methods include convolutional neural networks with fast inference speed but difficulty in learning image contextual features, and transformer with good performance but high hardware requirements. In this paper, we present a Patch Network (PNet) that incorporates the Swin Transformer notion into a convolutional neural network, allowing it to gather richer contextual information while achieving the balance of speed and accuracy. We test our PNet on Polyp(CVC-ClinicDB and ETIS- LaribPolypDB), Skin(ISIC-2018 Skin lesion segmentation challenge dataset) segmentation datasets. Our PNet achieves SOTA performance in both speed and accuracy.

翻译：医学图像的精确且快速分割在临床上至关重要，然而当前研究方法包括推理速度快但难以学习图像上下文特征的卷积神经网络，以及性能优异但硬件要求高的Transformer。本文提出了一种补丁网络（PNet），该网络将Swin Transformer概念融入卷积神经网络，使其能够在收集更丰富的上下文信息的同时实现速度与精度的平衡。我们在息肉分割数据集（CVC-ClinicDB和ETIS-LaribPolypDB）以及皮肤病变分割数据集（ISIC-2018皮肤病变分割挑战数据集）上测试了PNet。我们的PNet在速度和精度上均达到了最先进的性能。