标题： Dino U-Net：利用基础模型中的高保真密集特征进行医学图像分割 显示英文标题

标题： Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

摘要： 基于大规模自然图像数据集预训练的基础模型为医学图像分割提供了一个强大的范例。 然而，有效将它们的学习表示用于精确的临床应用仍然是一个挑战。 在本工作中，我们提出了Dino U-Net，一种新的编码器-解码器架构，旨在利用DINOv3视觉基础模型的高保真密集特征。 我们的架构引入了一个基于冻结的DINOv3主干的编码器，该编码器使用专门的适配器将模型的丰富语义特征与低级空间细节融合。 为了在降维过程中保持这些表示的质量，我们设计了一个新的保真度感知投影模块（FAPM），该模块能有效地精炼和投影特征以供解码器使用。 我们在七个不同的公共医学图像分割数据集上进行了广泛的实验。 我们的结果表明，Dino U-Net实现了最先进的性能，在各种成像模态中始终优于之前的方法。 我们的框架证明具有高度可扩展性，随着主干模型规模增加到70亿参数版本，分割准确性持续提高。 研究结果表明，利用通用基础模型的优越密集预训练特征提供了一种高效且参数高效的途径，以提高医学图像分割的准确性。 代码可在https://github.com/yifangao112/DinoUNet获取。 显示英文摘要

摘要： Foundation models pre-trained on large-scale natural image datasets offer a powerful paradigm for medical image segmentation. However, effectively transferring their learned representations for precise clinical applications remains a challenge. In this work, we propose Dino U-Net, a novel encoder-decoder architecture designed to exploit the high-fidelity dense features of the DINOv3 vision foundation model. Our architecture introduces an encoder built upon a frozen DINOv3 backbone, which employs a specialized adapter to fuse the model's rich semantic features with low-level spatial details. To preserve the quality of these representations during dimensionality reduction, we design a new fidelity-aware projection module (FAPM) that effectively refines and projects the features for the decoder. We conducted extensive experiments on seven diverse public medical image segmentation datasets. Our results show that Dino U-Net achieves state-of-the-art performance, consistently outperforming previous methods across various imaging modalities. Our framework proves to be highly scalable, with segmentation accuracy consistently improving as the backbone model size increases up to the 7-billion-parameter variant. The findings demonstrate that leveraging the superior, dense-pretrained features from a general-purpose foundation model provides a highly effective and parameter-efficient approach to advance the accuracy of medical image segmentation. The code is available at https://github.com/yifangao112/DinoUNet.