OmniRad: A Radiological Foundation Model for Multi-Task Medical Image Analysis
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.
69844334e34659da7e1f50b4
Luca Zedda
claimed_verified
2026-02-06T18:53:07.088Z
67bed13027ea9754039d63c3
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/JHlZXuwcY0y_estgKPkPT.jpeg
Luca Zedda
Snarcy
user
Snarcy
69844334e34659da7e1f50b5
Andrea Loddo
69844334e34659da7e1f50b6
Cecilia Di Ruberto
OmniRad: A Radiological Foundation Model for Multi-Task Medical Image Analysis
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.
OmniRad is a self-supervised radiological foundation model pretrained on 1.2 million medical images that demonstrates improved performance in classification and segmentation tasks through representation reuse and cross-task transferability.
pretrained visual representations self-supervised learning radiological foundation model downstream adaptation task-specific adapters end-to-end fine-tuning representation quality cross-task transferability MedMNISTv2 MedSegBench F1 score Dice score feature clustering latent-space visualization