Abstract
Self-supervised learning (SSL) has advanced visual representation learning, but its value in chest radiography, a high-volume imaging modality with fine-grained findings, remains unclear. Meta's DINOv3 extends earlier SSL models through Gram-anchored self-distillation. Whether these design choices improve transfer learning for chest radiography has not been systematically tested. We benchmarked DINOv3 against DINOv2 and ImageNet initialization across seven datasets (n>814,000). Two representative backbones were evaluated: ViT-B/16 and ConvNeXt-B. Images were analyzed at 224x224, 512x512, and 1024x1024 pixels. We additionally assessed frozen features from a 7B model. The primary outcome was mean AUROC across labels. At 224x224, DINOv3 and DINOv2 achieved comparable performance on adult datasets. Increasing resolution to 512x512 yielded consistent improvements for DINOv3 over both DINOv2 and ImageNet. In contrast, results in pediatric cohort showed no differences across initializations. Across all settings, ConvNeXt-B outperformed ViT-B/16. Models using frozen DINOv3-7B features underperformed relative to fully finetuned 86-89M-parameter backbones, highlighting the importance of domain adaptation. Scaling to 1024x1024 did not further improve accuracy. Resolution-related gains were most evident for boundary-dependent and small focal abnormalities. In chest radiography, higher input resolution is critical for leveraging the benefits of modern self-supervised models. 512x512 pixels represent a practical upper limit where DINOv3-initialized ConvNeXt-B networks provide the strongest performance, while larger inputs offer minimal return on cost. Clinically, these findings support use of finetuned, mid-sized backbones at 512x512 for chest radiograph interpretation, with the greatest gains expected in detecting subtle or boundary-centered lesions relevant to emergency and critical care settings.
Abstract (translated)
自监督学习（SSL）在视觉表征学习方面取得了进展，但在胸部X光摄影中的价值仍不清楚。胸部X光是一种高容量的影像模式，具有细微发现的特点。Meta的DINOv3通过Gram-锚定自我蒸馏扩展了早期的SSL模型。这些设计选择是否改善了胸部X光摄影中的迁移学习尚未被系统地测试过。我们在七个数据集（n>814,000）上对DINOv3与DINOv2和ImageNet初始化进行了基准比较。我们评估了两个代表性的骨干网络：ViT-B/16和ConvNeXt-B。图像在224x224、512x512和1024x1024像素下进行分析。我们还评估了来自7B模型的冻结特征。主要结果指标是标签的平均AUROC。 在224x224分辨率下，DINOv3与DINOv2在成人数据集上实现了相当的表现。增加到512x512像素后，DINOv3相对于DINOv2和ImageNet的一致改进变得更加明显。相比之下，在儿科群体中，不同初始化之间没有差异。在所有设置下，ConvNeXt-B优于ViT-B/16。使用冻结的DINOv3-7B特征的模型相对于完全微调后的86-89M参数骨干网络表现较弱，这突显了领域适应的重要性。分辨率增加到1024x1024后，并未进一步提高准确性。与边界相关的和小焦点异常的变化在分辨率相关增益中最明显。 在胸部X光摄影中，更高的输入分辨率对于利用现代自监督模型的好处至关重要。512x512像素代表了一个实际的上限，在这个分辨率下，由DINOv3初始化的ConvNeXt-B网络表现最佳，而更大的输入则无法提供额外的成本效益。临床上，这些发现支持使用在512x512上进行微调、中等大小骨干网络用于胸部X光片解释，并且预期在此类病变检测中的改进最为显著，特别是在急诊和重症监护环境中相关的细微或边界中心化病灶。
URL
https://arxiv.org/abs/2510.07191
