TomoTransformer: Towards a Foundation Model for CT Reconstruction

arXiv:2609.37605 · cs.CV, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-29

Updated: 2026-10-02

Terminology

• Transformer — 20× in this paper · explained in 2 episodes
• Sparsity — 9× in this paper · explained in 4 episodes
• Foundation Model — 8× in this paper · explained in 3 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Inverse Problem — 5× in this paper · explained in 2 episodes
• Attention Mechanism — 3× in this paper · explained in 3 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Distribution Shift — 3× in this paper · explained in 6 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation Studies — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Decoupling — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Operator Discretization Library — 2× in this paper
• Paul Scherrer Institute — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Swiss Data Science Center — 2× in this paper
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• EMMA — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Hallucination — 1× in this paper · explained in 4 episodes
• In International Conference on Learning Representations — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Masked Autoencoder (MAE) — 1× in this paper · explained in 5 episodes
• Swiss Light Source — 1× in this paper
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• On the Opportunities and Risks of Foundation Models
• CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
• The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

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