Beyond Report Imitation: Clinically Aware Multi-Image Ultrasound Report Generation from Visible Evidence

Key points

• Generating ultrasound reports from multiple images requires aggregating clinical evidence across views, yet archived key frames capture only part of the dynamic examination.1
• Raw-report imitation is therefore misaligned with visual supervision: content that is clinically valid for the full examination may be unverifiable from the images available to a model.1
• Stage I learns ultrasound visual-language primitives; Stage II performs Cross-View Evidence Grounding by distilling trusted visible report points into multi-image QA and report-style supervision; and Stage III performs Clinically Aware Preference Alignment using clinical-error-oriented preference pairs.1
• From USReport, we construct USReport-Distilled with 17,670 evidence-grounded paired-image training instances and USReport-Pref with 21,869 preference pairs; we additionally use 25,631 PubMedVision-US ultrasound instruction samples for domain adaptation and multi-image instruction tuning.1

Sources (1)

• [1]Beyond Report Imitation: Clinically Aware Multi-Image Ultrasound Report Generation from Visible Evidence
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 09:53 AM

  We propose CAMEO, a Clinically Aware Multi-image Evidence-grounded Orchestration framework for ultrasound report generation.

  Generating ultrasound reports from multiple images requires aggregating clinical evidence across views, yet archived key frames capture only part of the dynamic examination.

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