UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner
This work addresses the challenge of improving task-specific performance in universal medical image segmentation models, which is incremental but offers practical gains for medical imaging applications.
The authors tackled the problem of building a universal model for multi-task medical image segmentation by introducing a prompt-driven approach that makes the model aware of the task early, resulting in outperforming other models on 11 upstream tasks and beating pre-trained models on two downstream datasets.
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 'aware' 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 'aware' 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.