OmniRad是百万级医学影像自监督模型，支持多任务跨模态分析。
OmniRad: A Radiological Foundation Model for Multi-Task Medical Image Analysis
- 基于120万张医学图像自监督预训练，强调特征复用与跨任务迁移。
- 在MedMNISTv2上分类F1提升2.05%，六项分割任务平均Dice得分改善。
- 适合医学影像多任务建模，尤其擅长冻结主干时的轻量适配。
放射学分析日益受益于可支持异构下游任务的预训练视觉表示。本文提出OmniRad，一个在120万张医学图像上自监督预训练的放射科导向基础模型，设计原则强调表示复用与跨任务可迁移性。我们在多种下游适配范式下评估预训练编码器，包括冻结主干的轻量级任务特定适配器及全端到端微调，以衡量表示质量与任务性能。OmniRad在涵盖多种模态的分类与分割公共基准上进行评估。在MedMNISTv2数据集上，其分类F1相比现有基础模型最高提升2.05%。对于密集预测任务，使用冻结表示时，在六项MedSegBench数据集上均获得平均Dice分数提升。定性分析与潜在空间可视化表明，特征聚类更优，模态相关分离更清晰。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。