AI RESEARCH
SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation
arXiv CS.CL
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ArXi:2605.24371v1 Announce Type: cross 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.