arXiv Computer Vision
Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation
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arXiv AI
Sep 17
Generalist-Specialist Mixture-of-Experts for Rare Pathology Detection in Multimodal Imaging
The paper introduces Generalist‑Specialist Mixture‑of‑Experts (GS‑MoE), a two‑branch architecture that combines a cross‑modal generalist model with modality‑specific specialists through domain‑constrained feature fusion. GS‑MoE improves detection of rare pathologies in multimodal medical imaging, achieving significant per‑class F1 gains and outperforming dense and specialist‑only MoE baselines while using about 53% fewer active parameters at inference. The study demonstrates that balancing cross‑modal shared representations with expert routing can enhance performance on low‑prevalence conditions.
arXiv Computer Vision
Aug 25
Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation
arXiv:2409.16183v2 Announce Type: replace Abstract: Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in m...
arXiv Machine Learning
Aug 18
CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration
arXiv:2608. 16268v1 Announce Type: cross Abstract: Medical foundation models improve generalization when training AI models with limited labeled data, but remain confined to a single specialty, such as pathology or radiology, and to either sparse or dense outputs, such as classification or segmentation.
arXiv AI
Jul 13
ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts
arXiv:2607. 09526v1 Announce Type: cross Abstract: Foundation models are reshaping computational pathology, yet their capabilities remain shaped by pretraining objectives, data sources, and spatial scales, fragmenting complementary expertise across separate backbones.
arXiv Computer Vision
Sep 15
Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language
arXiv:2603.02790v2 Announce Type: replace Abstract: Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data m...
arXiv Computer Vision
Sep 24
nnFoundation: 3D Foundation Models for Radiology
nnFoundation introduces complementary convolutional and transformer-based 3D foundation models for radiology, trained on 2.1 million CT, MRI, and PET volumes from 125 datasets. The models are evaluated on 108 tasks—including segmentation, detection, classification, report generation, and image retrieval—under domain shift, low-data, and low-compute scenarios, consistently outperforming prior 3D foundation models and training from scratch. Performance varies by task type, with convolutional models excelling at spatially localized tasks and transformer models at global semantic reasoning, and dynamic alignment with dataset characteristics further enhances transferability.