research
The Scan Builds a Diagnosis From Findings
Diagnosis by Composition maps CT findings first, then combines them with fixed guideline rules to localize diseases that were unseen during training.
Summary
Diagnosis by Composition maps CT findings first, then combines them with fixed guideline rules to localize diseases that were unseen during training.
Instead of learning disease labels directly, the framework aligns image features with radiological findings and represents each finding as a dense map. A disease is defined by a guideline-derived set of required findings, and a fixed parameter-free composition produces the diagnosis map. On multiphase liver CT, the authors report better detection and localization of unseen diseases, rare diseases from another institution and an external cohort than tested comparators. The approach is auditable and can add a disease definition without retraining the visual representation. It remains a research result on liver CT, not a clinical validation across radiology.
Why it matters
Diagnosis by Composition maps CT findings first, then combines them with fixed guideline rules to localize diseases that were unseen during training.
Limits and context
- It remains a research result on liver CT, not a clinical validation across radiology.
Key claims
Diagnosis by Composition maps CT findings first, then combines them with fixed guideline rules to localize diseases that were unseen during training.
Qualification: It remains a research result on liver CT, not a clinical validation across radiology.
Evidence: source-2026-11-04-014
Sources
- arXiv preprint 2611.05231, original v1arXiv · primary research
Corrections
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