@inproceedings{e36011168b0243b2946d88eb8874b00e,
title = "UniSeg: A Prompt-Driven Universal Segmentation Model as Well as A Strong Representation Learner",
abstract = "The universal model emerges as a promising trend for medical image segmentation, paving up the way to build medical imaging large model (MILM). One popular strategy to build universal models is to encode each task as a one-hot vector and generate dynamic convolutional layers at the end of the decoder to extract the interested target. Although successful, it ignores the correlations among tasks and meanwhile is too late to make the model {\textquoteleft}aware{\textquoteright} of the ongoing task. To address both issues, we propose a prompt-driven Universal Segmentation model (UniSeg) for multi-task medical image segmentation using diverse modalities and domains. We first devise a learnable universal prompt to describe the correlations among all tasks and then convert this prompt and image features into a task-specific prompt, which is fed to the decoder as a part of its input. Thus, we make the model {\textquoteleft}aware{\textquoteright} of the ongoing task early and boost the task-specific training of the whole decoder. Our results indicate that the proposed UniSeg outperforms other universal models and single-task models on 11 upstream tasks. Moreover, UniSeg also beats other pre-trained models on two downstream datasets, providing the community with a high-quality pre-trained model for 3D medical image segmentation. Code and model are available at https://github.com/yeerwen/UniSeg.",
keywords = "Medical image segmentation, Prompt learning, Universal model",
author = "Yiwen Ye and Yutong Xie and Jianpeng Zhang and Ziyang Chen and Yong Xia",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 ; Conference date: 08-10-2023 Through 12-10-2023",
year = "2023",
doi = "10.1007/978-3-031-43898-1_49",
language = "英语",
isbn = "9783031438974",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "508--518",
editor = "Hayit Greenspan and Hayit Greenspan and Anant Madabhushi and Parvin Mousavi and Septimiu Salcudean and James Duncan and Tanveer Syeda-Mahmood and Russell Taylor",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings",
}