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 this https URL

本研究提出了一种基于可学习的通用提示的通用分割模型（UniSeg），用于多任务医学图像分割，融合多个模态和域，通过动态卷积层提取感兴趣的目标，从而实现了模型在早期意识到正在进行的任务，并提高了整个解码器的任务特定训练。在 11 个上游任务和 2 个下游任务中， UniSeg 相对于其他预训练模型和单任务模型表现更优秀。