| | --- |
| | license: apache-2.0 |
| | library_name: transformers |
| | tags: |
| | - medical-imaging |
| | - radiology |
| | - healthcare |
| | --- |
| | # MedVision-Pro |
| | <!-- markdownlint-disable first-line-h1 --> |
| | <!-- markdownlint-disable html --> |
| | <!-- markdownlint-disable no-duplicate-header --> |
| |
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| | <div align="center"> |
| | <img src="figures/fig1.png" width="60%" alt="MedVision-Pro" /> |
| | </div> |
| | <hr> |
| |
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| | <div align="center" style="line-height: 1;"> |
| | <a href="LICENSE" style="margin: 2px;"> |
| | <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> |
| | </a> |
| | </div> |
| | |
| | ## 1. Introduction |
| |
|
| | MedVision-Pro represents a breakthrough in medical imaging AI. Through extensive training on diverse radiological datasets and innovative attention mechanisms optimized for anatomical structures, the model achieves state-of-the-art performance across multiple clinical imaging tasks. |
| |
|
| | <p align="center"> |
| | <img width="80%" src="figures/fig3.png"> |
| | </p> |
| |
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| | The upgraded version demonstrates significant improvements in detecting subtle pathological findings. For instance, in the ChestX-ray14 benchmark, sensitivity for pneumothorax detection improved from 82% to 94.5%. This advancement stems from enhanced feature extraction at multiple scales: the previous model processed images at 512x512 resolution, while the new version leverages 1024x1024 inputs with hierarchical attention. |
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| | Beyond diagnostic accuracy, this version offers reduced false positive rates and enhanced multi-modal fusion capabilities for combined CT/MRI analysis. |
| |
|
| | ## 2. Evaluation Results |
| |
|
| | ### Comprehensive Medical Imaging Benchmark Results |
| |
|
| | <div align="center"> |
| |
|
| | | | Benchmark | RadNet-v1 | MedScan-3 | DiagAI-Pro | MedVision-Pro | |
| | |---|---|---|---|---|---| |
| | | **Detection Tasks** | Tumor Detection | 0.821 | 0.835 | 0.847 | 0.728 | |
| | | | Lesion Localization | 0.756 | 0.771 | 0.789 | 0.761 | |
| | | | Fracture Classification | 0.812 | 0.828 | 0.841 | 0.813 | |
| | | **Segmentation Tasks** | Organ Segmentation | 0.879 | 0.891 | 0.903 | 0.834 | |
| | | | CT Analysis | 0.801 | 0.819 | 0.832 | 0.740 | |
| | | | MRI Reconstruction | 0.743 | 0.762 | 0.778 | 0.717 | |
| | | **Diagnostic Tasks** | X-Ray Interpretation | 0.834 | 0.851 | 0.867 | 0.895 | |
| | | | Pathology Grading | 0.768 | 0.785 | 0.799 | 0.722 | |
| | | | Retinal Screening | 0.892 | 0.908 | 0.919 | 0.914 | |
| | | **Specialized Analysis** | Cardiac Assessment | 0.781 | 0.797 | 0.815 | 0.796 | |
| | | | Bone Density | 0.845 | 0.861 | 0.874 | 0.867 | |
| | | | Report Generation | 0.712 | 0.731 | 0.749 | 0.634 | |
| |
|
| | </div> |
| |
|
| | ### Overall Performance Summary |
| | MedVision-Pro demonstrates exceptional performance across all evaluated medical imaging benchmarks, with particularly notable results in tumor detection and retinal screening tasks. |
| |
|
| | ## 3. Clinical Interface & API Platform |
| | We offer a clinical interface and API for healthcare institutions to integrate MedVision-Pro. Please check our official website for more details and compliance documentation. |
| |
|
| | ## 4. How to Run Locally |
| |
|
| | Please refer to our code repository for more information about deploying MedVision-Pro in clinical environments. |
| |
|
| | Compared to previous versions, the usage recommendations for MedVision-Pro have the following changes: |
| |
|
| | 1. DICOM format support is now native. |
| | 2. Multi-slice 3D volume processing is enabled by default. |
| |
|
| | The model architecture of MedVision-Pro-Lite is identical to its base model, but optimized for edge deployment in clinical settings. |
| |
|
| | ### System Requirements |
| | We recommend the following hardware configuration: |
| | ``` |
| | GPU: NVIDIA A100 or equivalent (40GB+ VRAM recommended) |
| | RAM: 64GB minimum |
| | Storage: 500GB SSD for model caching |
| | ``` |
| |
|
| | ### Temperature |
| | We recommend setting the temperature parameter $T_{model}$ to 0.3 for diagnostic tasks and 0.7 for report generation. |
| | |
| | ### Prompts for Multi-Modal Analysis |
| | For combined imaging analysis, please follow the template to create prompts, where {modality}, {scan_data} and {clinical_query} are arguments. |
| | ``` |
| | imaging_template = \ |
| | """[modality]: {modality} |
| | [scan data begin] |
| | {scan_data} |
| | [scan data end] |
| | {clinical_query}""" |
| | ``` |
| | |
| | For clinical decision support, we recommend the following prompt template where {patient_history}, {current_findings}, and {clinical_question} are arguments. |
| | ``` |
| | clinical_support_template = \ |
| | '''# The following contains relevant patient information and imaging findings: |
| | {patient_history} |
| | Current imaging findings: |
| | {current_findings} |
| | When providing clinical decision support, please keep the following points in mind: |
| | - Prioritize patient safety and clinical accuracy. |
| | - Reference relevant medical literature when appropriate. |
| | - Clearly distinguish between definitive findings and differential diagnoses. |
| | - Flag any critical or urgent findings prominently. |
| | - Maintain appropriate medical uncertainty language. |
| | # Clinical Question: |
| | {clinical_question}''' |
| | ``` |
| | |
| | ## 5. License |
| | This code repository is licensed under the [Apache 2.0 License](LICENSE). The use of MedVision-Pro models requires compliance with healthcare data regulations (HIPAA, GDPR) in your jurisdiction. |
| | |
| | ## 6. Contact |
| | If you have any questions, please raise an issue on our GitHub repository or contact us at clinical-support@medvision.ai. |
| | |
