@inproceedings{f6002b0882464ff9a2de263cff5abc57,
title = "UNet-2022: Exploring Dynamics in Non-isomorphic Architecture",
abstract = "In this paper, we first analyze the differences between the weight allocation mechanisms of the self-attention and convolution. Based on this analysis, we propose to construct a parallel non-isomorphic block that takes the advantages of self-attention and convolution with simple parallelization. We name the resulting U-shape segmentation model as UNet-2022. In experiments, UNet-2022 obviously outperforms its counterparts in a range segmentation tasks, including abdominal multi-organ segmentation, automatic cardiac diagnosis, neural structures segmentation, and skin lesion segmentation, sometimes surpassing the best performing baseline by 4\%. Specifically, UNet-2022 surpasses nnUNet, the most recognized segmentation model at present, by large margins. These phenomena indicate the potential of UNet-2022 to become the model of choice for medical image segmentation.",
keywords = "Medical image segmenation, Transformer",
author = "Jiansen Guo and Zhou, \{Hong Yu\} and Liansheng Wang and Yizhou Yu",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2022 ; Conference date: 20-11-2022 Through 21-11-2022",
year = "2023",
doi = "10.1007/978-981-16-6775-6\_38",
language = "英语",
isbn = "9789811667749",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "465--476",
editor = "Ruidan Su and Yudong Zhang and Han Liu and \{F Frangi\}, Alejandro",
booktitle = "Medical Imaging and Computer-Aided Diagnosis - Proceedings of 2022 International Conference on Medical Imaging and Computer-Aided Diagnosis MICAD 2022",
address = "德国",
}