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.
Scaling self-supervised and cross-modal pretraining for volumetric ct transformers
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
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UNVERDICTED 2representative citing papers
A unified Sparse Vision Transformer learns joint 2D/3D medical image representations via self-supervision and achieves competitive AUROC on chest X-ray and CT benchmarks with 5x less data than modality-specific models.
citing papers explorer
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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.
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MultiMedVision: Multi-Modal Medical Vision Framework
A unified Sparse Vision Transformer learns joint 2D/3D medical image representations via self-supervision and achieves competitive AUROC on chest X-ray and CT benchmarks with 5x less data than modality-specific models.