用140万张乳腺影像训练新模型，提升癌症筛查效率。
MammoDINO: Anatomically Aware Self-Supervision for Mammographic Images
- 设计乳腺组织感知的数据增强采样与跨切片对比学习。
- 在五个数据集上达到顶尖性能，泛化能力强。
- 无需标注，适合构建多功能乳腺癌辅助诊断工具。
自监督学习（SSL）已革新通用视觉编码器的训练，但在医学影像中因数据有限和领域偏见而应用不足。我们提出MammoDINO，一个针对乳腺摄影的新型SSL框架，在140万张乳腺影像上预训练。为捕捉临床相关特征，引入乳腺组织感知的数据增强采样器，用于图像级和局部块级监督，并设计跨切片对比学习目标，将三维数字乳腺断层成像（DBT）结构融入二维预训练。MammoDINO在多个乳腺癌筛查任务中表现领先，且在五个基准数据集上泛化良好。该模型提供可扩展、无需标注的基础，适用于多种乳腺影像辅助诊断工具，有助于减轻放射科医生负担，提升筛查效率。
原文摘要 · Abstract (English)
Self-supervised learning (SSL) has transformed vision encoder training in general domains but remains underutilized in medical imaging due to limited data and domain specific biases. We present MammoDINO, a novel SSL framework for mammography, pretrained on 1.4 million mammographic images. To capture clinically meaningful features, we introduce a breast tissue aware data augmentation sampler for both image-level and patch-level supervision and a cross-slice contrastive learning objective that leverages 3D digital breast tomosynthesis (DBT) structure into 2D pretraining. MammoDINO achieves state-of-the-art performance on multiple breast cancer screening tasks and generalizes well across five benchmark datasets. It offers a scalable, annotation-free foundation for multipurpose computer-aided diagnosis (CAD) tools for mammogram, helping reduce radiologists' workload and improve diagnostic efficiency in breast cancer screening.
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