Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation
arXiv:2605.30716v1 Announce Type: cross Abstract: Generating clinically useful pathology reports for pathology cases from whole-slide images (WSIs) is challenging due to gigapixel resolution, long visual-token sequences, and the complexity of case-level reasoning, where a single case may contain multiple WSIs with heterogeneous tissues and ambiguous findings. We present a simple token-efficient vision--language model for case-level synoptic report generation that remains practical under constrained GPU memory. Our architecture follows a minimal three-component design: a frozen pathology patch
Advances in vision-language models and the increasing availability of digitized pathology data are enabling the development of practical AI solutions for medical diagnostics.
This development can significantly improve the efficiency and accuracy of pathology diagnoses, reducing human error and accelerating patient care, particularly in resource-intensive areas like cancer detection.
The ability to generate comprehensive pathology reports from complex visual data using token-efficient models makes advanced AI more accessible for clinical applications, even under memory constraints.
- · Healthcare providers
- · AI developers specializing in medical imaging
- · Patients needing pathology diagnoses
- · Pathology labs
- · Traditional pathology software vendors
- · Diagnostic services relying solely on manual review
Increased adoption of AI tools in pathology departments for automated report generation.
Improved standardization and reduced turnaround times for diagnostic reports across healthcare systems.
Enhanced AI-driven drug discovery and personalized medicine as pathology data becomes more structured and interpretable by machines.
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Read at arXiv cs.AI