SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation

• URL: http://arxiv.org/abs/2605.24371v1
• Date: Sat, 23 May 2026 03:18:56 GMT
• Title: SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation
• Abstract summary: We propose SliceWorld, a CT-specific world-state framework that treats an axial CT scan as an ordered sequence along the z-axis.<n>We show that SliceWorld improves natural language generation metrics and clinically oriented automatic evaluation.
• Score: 22.24208120178663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: CT report generation (CTRG) requires models to summarize three-dimensional anatomical context and pathological findings from hundreds of axial slices. Existing methods typically learn a direct image-to-text mapping, providing limited mechanisms for modeling how CT evidence evolves across slices or how reports respond to controlled changes in latent lesion-related factors. We propose SliceWorld, a CT-specific world-state framework that treats an axial CT scan as an ordered sequence along the z-axis. SliceWorld encodes prefix CT evidence into factor-aware latent states containing anatomy, lesion, and uncertainty components, and projects these states into world tokens used for multi-step future-slice feature prediction, lesion-factor intervention, and LLM-based report generation. The model is first pretrained on CT slice sequences with predictive, factor-aware, and counterfactual objectives, and is then fine-tuned on paired CT-report data. Experiments on M3D-Cap and CT-RATE show that SliceWorld improves natural language generation metrics and clinically oriented automatic evaluation. Further analyses demonstrate multi-horizon future-slice prediction, measurable factor alignment, reduced-slice robustness, and selective lesion-sensitive report modulation.

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