Abstract
Deep learning-based automatic medical image segmentation plays a critical role in clinical diagnosis and treatment planning but remains challenging in few-shot scenarios due to the scarcity of annotated training data. Recently, self-supervised foundation models such as DINOv3, which were trained on large natural image datasets, have shown strong potential for dense feature extraction that can help with the few-shot learning challenge. Yet, their direct application to medical images is hindered by domain differences. In this work, we propose DINO-AugSeg, a novel framework that leverages DINOv3 features to address the few-shot medical image segmentation challenge. Specifically, we introduce WT-Aug, a wavelet-based feature-level augmentation module that enriches the diversity of DINOv3-extracted features by perturbing frequency components, and CG-Fuse, a contextual information-guided fusion module that exploits cross-attention to integrate semantic-rich low-resolution features with spatially detailed high-resolution features. Extensive experiments on six public benchmarks spanning five imaging modalities, including MRI, CT, ultrasound, endoscopy, and dermoscopy, demonstrate that DINO-AugSeg consistently outperforms existing methods under limited-sample conditions. The results highlight the effectiveness of incorporating wavelet-domain augmentation and contextual fusion for robust feature representation, suggesting DINO-AugSeg as a promising direction for advancing few-shot medical image segmentation. Code and data will be made available on this https URL.
Abstract (translated)
基于深度学习的自动医学图像分割在临床诊断和治疗计划中扮演着关键角色，但在标注训练数据稀缺的情况下进行的少样本场景下仍面临挑战。最近，像DINOv3这样的自监督基础模型，在大型自然图像数据集上训练后，显示出强大的密集特征提取能力，有助于解决少样本学习问题。然而，由于领域差异，直接将其应用于医学影像受限。 为此，我们提出了一种名为DINO-AugSeg的新框架，该框架利用DINOv3特征来应对少样本医疗影像分割的挑战。具体来说，我们引入了WT-Aug模块，这是一个基于小波变换的增强模块，通过扰动频率成分丰富DINOv3提取到的特征多样性；同时设计了一个CG-Fuse模块，这是一种指导上下文信息融合的模块，利用跨注意力机制将语义丰富的低分辨率特征与空间细节丰富的高分辨率特征相整合。 我们在包括MRI、CT、超声波、内窥镜和皮肤镜在内的六项公开基准测试中进行了广泛的实验。结果显示，在样本受限条件下，DINO-AugSeg持续优于现有的方法。这些结果强调了结合小波领域增强和上下文融合对于稳健的特征表示的有效性，并表明DINO-AugSeg为推进少样本医学影像分割提供了有前景的方向。 相关代码与数据将在提供的网址上公开发布：[https://this-url.com](https://this-url.com)（注意，这里使用的URL是示例性质的，请替换为您实际提供的链接）。
URL
https://arxiv.org/abs/2601.08078
