Seeing Through Multiple Views: Parameter-Efficient Fine-Tuning via Selective Neurons for Consistent Radiology Report Generation
arXiv:2606.31099v1 Announce Type: cross Abstract: Recent years have seen substantial advances in radiology report generation (RRG), yet existing approaches predominantly adopt direct feature fusion when handling multi-view X-ray images. Such approaches overlook the potential clinical inconsistencies and inaccuracies arising when a single model processes different views, adversely impacting performance and clinical reliability. To this end, we introduce View-PNDF (View-specific Pattern Neuron Detection and Fine-tuning), a parameter-efficient framework that fosters view-consistent report generat
The rapid advancements in AI, particularly foundational models, are making specialized applications like radiology report generation a focal point for performance and reliability improvements.
Improving the accuracy and consistency of AI-generated medical reports directly impacts patient care, diagnostic reliability, and the broader integration of AI into critical healthcare systems.
This research introduces a more robust and parameter-efficient method for generating radiology reports, potentially accelerating the deployment of reliable AI in medical diagnostics.
- · Healthcare AI developers
- · Radiology departments
- · Patients
- · AI agents in healthcare
- · Traditional, less precise AI report generation methods
More accurate and consistent automated radiology reports become available to clinicians.
Increased trust in AI diagnostics leads to broader adoption and integration of AI into clinical workflows.
The enhanced diagnostic capabilities offered by reliable AI shift the demand for human radiologists towards more complex case review and interventional procedures.
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Read at arXiv cs.AI