Medical Imaging AI
PlaqueCap: lesion-centered captioning of atherosclerotic plaques in intravascular ultrasound using vision-language models and prompt injection
PlaqueCap introduces a novel, lesion-centered captioning framework for intravascular ultrasound (IVUS) images, generating clinically meaningful natural language descriptions of atherosclerotic plaques. By combining high-fidelity segmentation with a Lesion Prompt Injection (LPI) module, it grounds text generation in specific plaque pathologies. This approach significantly outperforms traditional methods and existing vision-language models in accuracy and clinical interpretability, offering a paradigm for explainable AI in interventional cardiology.
Revolutionizing IVUS Analysis with Explainable AI
PlaqueCap delivers quantifiable improvements in accuracy, interpretability, and efficiency for cardiovascular diagnostics.
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BLEU-4 Score
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CIDEr Score
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DSC Score (Segmentation)
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Reduction in Reporting Time
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
PlaqueCap Workflow
Performance Metrics
LPI Module
Qualitative Advantage
PlaqueCap's End-to-End Workflow
PlaqueCap processes IVUS images through a multi-stage pipeline, from precise plaque localization to natural language report generation.
IVUS Image Input
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Plaque Segmentation (Hierarchical Transformer)
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SAM Image Encoding & Prompt Extraction
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Lesion Prompt Injection (LPI) Module
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Q-Former & LLM Decoding
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Clinical Caption Output
| Model | BLEU-4 | CIDEr | METEOR | ROUGE-L |
|---|---|---|---|---|
| BLIP-2 | 27.1% | 74.2% | 21.4% | 46.0% |
| GIT | 25.6% | 70.1% | 20.5% | 45.3% |
| LLaVA | 22.7% | 64.5% | 19.2% | 42.5% |
| MiniGPT-4 | 21.3% | 61.8% | 18.6% | 41.2% |
| BioViL-T | 28.5% | 75.9% | 22.0% | 46.8% |
| PlaqueCap (Ours) | 33.8% | 82.4% | 24.6% | 50.5% |
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CIDEr point improvement with LPI module vs. no segmentation guidance
The LPI module is the cornerstone of PlaqueCap, enabling the model to ground its textual generation in specific visual features of the identified lesion.
PlaqueCap's Qualitative Advantage: Lipid-Rich Plaque
Baseline: 'This cross-section of a blood vessel shows thickening on the upper wall, containing a mix of bright.'
PlaqueCap: 'A circumferential, lipid-rich plaque is observed, encircling the entire lumen. It is identified by its homogeneous and distinctly hypoechoic appearance without posterior acoustic shadowing.'
PlaqueCap provides a far more detailed and diagnostically relevant description, highlighting its ability to interpret subtle pathological features.
Advanced ROI Calculator
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap
A structured approach to integrating PlaqueCap into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Needs Assessment & Data Integration
Collaborate with clinical teams to define specific reporting requirements and integrate PlaqueCap with existing IVUS data pipelines. Establish secure, compliant data transfer protocols.
Phase 2: Customization & Fine-tuning
Tailor PlaqueCap's models to your institution's specific IVUS imaging protocols and reporting standards, leveraging your unique data for optimal performance.
Phase 3: Validation & Clinical Pilot
Conduct a rigorous internal validation study with expert cardiologists. Deploy PlaqueCap in a controlled pilot environment to gather real-world feedback and refine workflows.
Phase 4: Full Deployment & Training
Scale PlaqueCap across your cardiology department. Provide comprehensive training to clinicians and support staff on using the new AI-powered reporting system effectively.
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