Radiological analysis increasingly benefits from pretrained visual representations that can support heterogeneous downstream tasks across imaging modalities. In this work, we introduce OmniRad, a self-supervised radiological foundation model pretrained on 1.2 million medical images, designed with radiology-inspired principles emphasizing representation reuse and cross-task transferability. We evaluate the pretrained encoder under multiple downstream adaptation regimes, including lightweight task-specific adapters with a frozen backbone as well as full end-to-end fine-tuning for classification, allowing us to assess both representation quality and task-specific performance. OmniRad is evaluated on a broad suite of public benchmarks spanning classification and segmentation across multiple modalities. On the MedMNISTv2 collection, OmniRad improves classification F1 by up to 2.05% over competing foundation models. For dense prediction, OmniRad attains mean Dice score improvements across six MedSegBench datasets when using frozen representations. Qualitative analyses and latent-space visualizations suggest improved feature clustering and modality-related separation.

翻译：放射学分析日益受益于预训练的视觉表征，这些表征能够支持跨影像模态的异质下游任务。本文提出OmniRad，一种基于放射学原理设计的自监督放射学基础模型，该模型在120万张医学影像上进行预训练，强调表征复用与跨任务可迁移性。我们在多种下游适应机制下评估预训练编码器，包括使用冻结主干网络的轻量级任务特定适配器以及用于分类的端到端全微调，从而同时评估表征质量与任务特定性能。OmniRad在涵盖多模态分类与分割的广泛公共基准测试套件上进行评估。在MedMNISTv2数据集中，OmniRad相比竞争性基础模型将分类F1分数最高提升2.05%。在密集预测任务中，使用冻结表征时OmniRad在六个MedSegBench数据集上实现了平均Dice分数的提升。定性分析与隐空间可视化表明其具有更优的特征聚类与模态相关分离特性。