SelfCue: Making a 3D CT Report Generator Say What It Already Knows

arXiv:2609.31788 · cs.CV · Submitted 2026-09-25 · Read on arXiv

cs.CV

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/renjie-liang/SelfCue-CT

Terminology

• AUROC — 18× in this paper · explained in 2 episodes
• Distillation — 15× in this paper · explained in 2 episodes
• Decode — 14× in this paper · explained in 2 episodes
• Knowledge Distillation — 4× in this paper · explained in 11 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Hallucination — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• The Knowing-Saying Gap: When Probes See Errors that Confidence Misses
• The Llama 3 Herd of Models
• Distilling the Knowledge in a Neural Network
• The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study
• When Do Cheap Probes Predict Expensive Training? Probing 3D-CT Encoders for Text Generation
• Beyond the Embedding Bottleneck: Adaptive Retrieval-Augmented 3D CT Report Generation
• ORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression
• Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes
• Generating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation
• RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography
• MedGemma Technical Report
• U-VLM: Hierarchical Vision Language Modeling for Report Generation
• Learning by Distilling Context
• SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation
• Not All Tokens Matter Equally: Dynamic In-context Vector Distillation with Decisive-Token Supervision for Long-form Medical Report Generation

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