Upload best model checkpoint (epoch_25) with clinical benchmark results

README.md

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+---
+license: apache-2.0
+library_name: transformers
+---
+# MedVision-DiagnosticsAI
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<div align="center">
+  <img src="figures/architecture.png" width="60%" alt="MedVision-DiagnosticsAI" />
+</div>
+<hr>
+
+<div align="center" style="line-height: 1;">
+  <a href="LICENSE" style="margin: 2px;">
+    <img alt="License" src="figures/license_badge.png" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+## 1. Introduction
+
+MedVision-DiagnosticsAI represents a breakthrough in medical imaging analysis, leveraging state-of-the-art Vision Transformer (ViT) architecture for multi-modal diagnostic tasks. The model has been extensively fine-tuned on diverse medical imaging datasets including X-rays, CT scans, and MRI images.
+
+<p align="center">
+  <img width="80%" src="figures/performance_chart.png">
+</p>
+
+Our model achieves remarkable performance on several clinical benchmarks, demonstrating its potential for assisting healthcare professionals in diagnostic workflows. The architecture combines attention mechanisms with domain-specific pre-training to capture subtle patterns in medical imagery.
+
+Key features of MedVision-DiagnosticsAI:
+- Multi-modal medical image classification
+- High sensitivity for early disease detection
+- Calibrated uncertainty estimates
+- HIPAA-compliant deployment options
+
+## 2. Evaluation Results
+
+### Comprehensive Benchmark Results
+
+<div align="center">
+
+| | Benchmark | Baseline | ModelA | ModelB-v2 | MedVision-DiagnosticsAI |
+|---|---|---|---|---|---|
+| **Classification Tasks** | Chest X-Ray Classification | 0.821 | 0.845 | 0.867 | 0.892 |
+| | CT Scan Analysis | 0.756 | 0.778 | 0.801 | 0.844 |
+| | MRI Segmentation | 0.698 | 0.721 | 0.745 | 0.856 |
+| **Detection Tasks** | Tumor Detection | 0.812 | 0.834 | 0.851 | 0.889 |
+| | Anomaly Localization | 0.745 | 0.768 | 0.789 | 0.819 |
+| | Lesion Identification | 0.789 | 0.812 | 0.835 | 0.896 |
+| **Clinical Metrics** | Sensitivity | 0.867 | 0.889 | 0.901 | 0.932 |
+| | Specificity | 0.834 | 0.856 | 0.878 | 0.894 |
+| | PPV (Precision) | 0.812 | 0.834 | 0.856 | 0.877 |
+| | NPV | 0.845 | 0.867 | 0.889 | 0.914 |
+| **Robustness** | Cross-Domain Transfer | 0.678 | 0.701 | 0.723 | 0.787 |
+| | Noise Resilience | 0.712 | 0.734 | 0.756 | 0.797 |
+| | Calibration Error | 0.089 | 0.078 | 0.067 | 0.065 |
+
+</div>
+
+### Overall Performance Summary
+MedVision-DiagnosticsAI demonstrates exceptional performance across all evaluated clinical benchmarks, with particularly strong results in sensitivity and multi-modal classification tasks.
+
+## 3. Clinical Applications
+Our model is designed to assist healthcare professionals in:
+- Rapid screening of chest X-rays
+- CT scan abnormality detection
+- MRI-based tissue analysis
+- Cross-modality diagnostic support
+
+## 4. How to Run Locally
+
+Please refer to our code repository for detailed instructions on running MedVision-DiagnosticsAI locally.
+
+### System Requirements
+- GPU with at least 8GB VRAM
+- Python 3.8+
+- transformers >= 4.30.0
+
+### Quick Start
+```python
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+
+model = AutoModelForImageClassification.from_pretrained("your-org/MedVision-DiagnosticsAI")
+processor = AutoImageProcessor.from_pretrained("your-org/MedVision-DiagnosticsAI")
+
+# Process your medical image
+inputs = processor(images=your_image, return_tensors="pt")
+outputs = model(**inputs)
+```
+
+### Inference Parameters
+We recommend the following settings for optimal performance:
+- Batch size: 1 (for clinical applications)
+- Image size: 224x224
+- Normalization: ImageNet statistics
+
+## 5. License
+This model is licensed under the [Apache 2.0 License](LICENSE). The model is intended for research and clinical decision support only.
+
+## 6. Contact
+If you have any questions, please raise an issue on our GitHub repository or contact us at support@medvision-ai.org.
+
+## 7. Citation
+```bibtex
+@article{medvision2025,
+  title={MedVision-DiagnosticsAI: A Multi-Modal Medical Imaging Foundation Model},
+  author={MedVision Team},
+  journal={arXiv preprint},
+  year={2025}
+}
+```

config.json

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+{
+    "model_type": "vit",
+    "architectures": ["ViTForImageClassification"],
+    "num_labels": 5,
+    "image_size": 224
+}

pytorch_model.bin

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