Learning How Much, Not Just What: Cross-Patient Burden Order for CT Vision-Language Pretraining
Volumetric CT vision-language pretraining learns 3D representations from scan-report pairs, but global and anatomy-aware objectives supervise only correspondence: they establish what is present and leave how much unconstrained. Nothing separates a mild from an extensive case of the same finding along a consistent direction, so the graded burden language in reports collapses into a present/absent signal. Longitudinal supervision would supply this order, but patient-matched CT pairs are scarce at scale; cross-sectional cohorts already encode weak burden cues across different patients. We introduce Spectrum, an anatomy-conditioned framework that represents each study at whole-study and organ...
| Authors | Guoliang You, Haifan Gong, Xiaomeng Chu |
|---|---|
| Primary category | cs.CV |
| Published | 31 July 2026 |
| Last updated at source | 16 September 2026 |
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