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README.md

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+---
+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 -->
+
+<div align="center">
+  <img src="figures/fig1.png" width="60%" alt="MedVision-Pro" />
+</div>
+<hr>
+
+<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>
+
+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.
+
+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.

config.json

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+{
+  "model_type": "medvision-pro",
+  "architectures": [
+    "MedVisionForImageClassification"
+  ],
+  "hidden_size": 1024,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "intermediate_size": 4096,
+  "image_size": 1024,
+  "patch_size": 16,
+  "num_channels": 3,
+  "num_labels": 14,
+  "layer_norm_eps": 1e-06,
+  "dropout_rate": 0.1,
+  "attention_dropout_rate": 0.0,
+  "initializer_range": 0.02,
+  "epoch": 100,
+  "training_steps": 500000,
+  "learning_rate": 5.987369392383787e-05
+}

pytorch_model.bin

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