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We introduce meddinov3, a simple and effective framework for adapting dinov3 to medical segmentation 在CT-3M（一个大规模的CT图像集）上对MedDINOv3进行了域自适应预训练，以更好地将模型与放射学数据分布相匹配 gram锚定，一种防止局部特征崩溃的机制，是我们的预训练框架可选 提出了MedDINOv3框架：一个简单而高效的框架，通过架构适配和领域预训练，成功释放了视觉基础模型在医学分割任务上的潜力。
MEDDINOV3: HOW TO ADAPT VISION FOUNDATION MODELS FOR MEDICAL IMAGE SEGMENTATION? CT和 MRI 扫描中器官和肿瘤的准确分割对于诊断、治疗计划和疾病监测至关重要 MedDINOv3的成功并非依赖复杂的创新，而是通过 “架构轻量化优化”+“数据规模化适配” 的组合策略，精准解决了视觉基础模型在医学分割中的核心痛点。 MedDINOv3是一个将DINOv3与多尺度令牌聚合相结合的框架，通过克服领域适应和骨干性能方面的挑战，在医学图像分割方面取得了最先进的性能。
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