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:医学图象的准确和快速分解是临床上必不可少的,但目前的研究方法包括快速推断速度但难以学习图像背景特征的进化神经网络和性能良好但硬件要求高的变压器。在本文中,我们展示了将Swin变形器概念纳入进化神经网络的补丁网络(PNet),使其能够收集更丰富的背景信息,同时实现速度和准确性之间的平衡。我们测试了我们关于聚合(CVC-ClinicDB和ETIS-LaribPollypDB)的PNet、Skin(ISIC-2018 Skin 皮肤分解挑战数据集)的分解数据集。我们的PNet在速度和准确性两方面都取得了SOTA的性能。