SliceWorld introduces a world-state model for CT report generation that uses predictive and factor-aware objectives on axial slice sequences.
Scaling self-supervised and cross-modal pretraining for volumetric ct transformers
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
ConQuer augments global CLIP alignment with independent per-concept contrastive losses on anatomical regions extracted from reports, producing Jolia which outperforms CLIP baselines on classification, report generation, and transfer.
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.
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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SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation
SliceWorld introduces a world-state model for CT report generation that uses predictive and factor-aware objectives on axial slice sequences.
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Jolia: Concept-Level Vision-Language Alignment for 3D CT Contrastive Learning
ConQuer augments global CLIP alignment with independent per-concept contrastive losses on anatomical regions extracted from reports, producing Jolia which outperforms CLIP baselines on classification, report generation, and transfer.
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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.