Public articles linked to the same research event.
arXiv The study introduces TotalFM, an organ-separated 3D-CT radiology foundation model that uses TotalSegmentator and LLMs to automatically build roughly 340,000 organ-level volume-text pairs, then combines VideoMAE self-supervised pre-training with organ-wise contrastive learning; it reaches an average F1 of 0.708 in zero-shot organ-wise lesion classification (versus 0.515 for CT-CLIP and 0.650 for Merlin), a higher AUROC than Merlin in 83% (25/30) of finding categories, and report-generation performance comparable to Merlin while raising batch efficiency to 32 batch/GPU.
The study introduces TotalFM, an organ-separated 3D-CT radiology foundation model that uses TotalSegmentator and LLMs to automatically build roughly 340,000 organ-level volume-text pairs, then combines VideoMAE self-supervised pre-training with organ-wise contrastive learning; it reaches an average F1 of 0.708 in zero-shot organ-wise lesion classification (versus 0.515 for CT-CLIP and 0.650 for Merlin), a higher AUROC than Merlin in 83% (25/30) of finding categories, and report-generation performance comparable to Merlin while raising batch efficiency to 32 batch/GPU.
The study introduces TotalFM, an organ-separated 3D-CT radiology foundation model that uses TotalSegmentator and LLMs to automatically build roughly 340,000 organ-level volume-text pairs, then combines VideoMAE self-supervised pre-training with organ-wise contrastive learning; it reaches an average F1 of 0.708 in zero-shot organ-wise lesion classification (versus 0.515 for CT-CLIP and 0.650 for Merlin), a higher AUROC than Merlin in 83% (25/30) of finding categories, and report-generation performance comparable to Merlin while raising batch efficiency to 32 batch/GPU.
The study introduces TotalFM, an organ-separated 3D-CT radiology foundation model that uses TotalSegmentator and LLMs to automatically build roughly 340,000 organ-level volume-text pairs, then combines VideoMAE self-supervised pre-training with organ-wise contrastive learning; it reaches an average F1 of 0.708 in zero-shot organ-wise lesion classification (versus 0.515 for CT-CLIP and 0.650 for Merlin), a higher AUROC than Merlin in 83% (25/30) of finding categories, and report-generation performance comparable to Merlin while raising batch efficiency to 32 batch/GPU.