Vision Foundation Models for Computed Tomography
read the original abstract
Foundation models (FMs) have shown transformative potential in radiology by performing diverse, complex tasks across imaging modalities. Here, we developed CT-FM, a large-scale 3D image-based pre-trained model designed explicitly for various radiological tasks. CT-FM was pre-trained using 148,000 computed tomography (CT) scans from the Imaging Data Commons through label-agnostic contrastive learning. We evaluated CT-FM across four categories of tasks, namely, whole-body and tumor segmentation, head CT triage, medical image retrieval, and semantic understanding, showing superior performance against state-of-the-art models. Beyond quantitative success, CT-FM demonstrated the ability to cluster regions anatomically and identify similar anatomical and structural concepts across scans. Furthermore, it remained robust across test-retest settings and indicated reasonable salient regions attached to its embeddings. This study demonstrates the value of large-scale medical imaging foundation models and by open-sourcing the model weights, code, and data, aims to support more adaptable, reliable, and interpretable AI solutions in radiology.
This paper has not been read by Pith yet.
Forward citations
Cited by 8 Pith papers
-
Learning from Compressed CT: Feature Attention Style Transfer and Structured Factorized Projections for Resource-Efficient Medical Image Analysis
CT-Lite combines Feature Attention Style Transfer (FAST) and Structured Factorized Projections (SFP) with contrastive learning to reach AUROC within 5-7% of uncompressed baselines on compressed CT volumes across three...
-
DALE-CT: Depth-Aware Foundation Models for Computed Tomography
DALE-CT, a 2D LeJEPA model with depth-aware dual supervision, reaches 0.833 Macro AUROC on multi-abnormality detection in CT and approaches 3D SOTA performance using less data and no textual supervision.
-
Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining
FlexiCT applies agglomerative pretraining across 2D, 3D, and vision-language stages on a large public CT collection to produce representations that match or exceed specialized models on segmentation, classification, r...
-
JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift
JANUS conditions Vision Transformer embeddings on macro-radiomic priors via anatomically guided gating, reaching macro-AUROC 0.88 on an internal test set of 5082 cases and 0.87 on an external set of 2000 cases while i...
-
Learning Robust Visual Features in Computed Tomography Enables Efficient Transfer Learning for Clinical Tasks
VoxelFM learns robust 3D CT visual features via DINO self-distillation that transfer effectively to seven clinical task categories using frozen backbones and lightweight heads, outperforming prior CT foundation models...
-
Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining
FlexiCT provides CT foundation models via agglomerative pretraining on 266227 volumes from 56 datasets that match or exceed task-specific models on five task families while organizing embeddings along tumor-stage gradients.
-
Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning
ASSFT combines active test-time sample selection via diversified knowledge divergence and anatomical segmentation difficulty with selective semi-supervised fine-tuning to adapt medical vision foundation models for vol...
-
Benchmarking Foundation Models for Renal Lesion Stratification in CT
Medical foundation models match a ResNet-50 but are outperformed by radiomics (AUC 0.88) on external validation for renal lesion stratification in CT.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.