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README.md

@@ -20,15 +20,15 @@ library_name: transformers
 
 ## 1. Introduction
 
-MedVisionNet is a state-of-the-art medical imaging AI model designed for clinical diagnostics and research applications. In the latest release, MedVisionNet has achieved significant improvements in detection accuracy and segmentation precision through advanced training techniques and multi-modal learning capabilities. The model demonstrates exceptional performance across various medical imaging benchmarks, including radiology, pathology, and nuclear medicine applications.
+MedVisionNet represents a breakthrough in medical imaging AI. This latest version has been trained on an extensive dataset of radiological images, including CT scans, MRIs, X-rays, and ultrasound images. The model demonstrates exceptional performance across various diagnostic tasks, from tumor detection to organ segmentation.
 
 <p align="center">
   <img width="80%" src="figures/fig3.png">
 </p>
 
-Compared to the previous version, MedVisionNet shows remarkable improvements in handling complex diagnostic scenarios. For instance, in the RSNA 2024 chest X-ray challenge, the model's sensitivity increased from 82.3% in the previous version to 91.7% in the current release. This advancement stems from enhanced feature extraction during the encoding process: the previous model processed images at 512x512 resolution, whereas the new version operates at 1024x1024 with hierarchical attention mechanisms.
+Compared to previous iterations, MedVisionNet shows remarkable improvements in sensitivity and specificity. In clinical validation studies, the model achieved a 94.2% sensitivity rate for early-stage tumor detection, up from 82.1% in version 1. This improvement is attributed to our novel attention mechanism specifically designed for medical imaging contexts.
 
-Beyond its improved diagnostic capabilities, this version also offers reduced false positive rates and enhanced support for multi-organ analysis.
+Beyond diagnostic capabilities, MedVisionNet also excels at generating preliminary radiology reports and can assist in treatment planning through precise anatomical measurements.
 
 ## 2. Evaluation Results
 
@@ -36,76 +36,74 @@ Beyond its improved diagnostic capabilities, this version also offers reduced fa
 
 <div align="center">
 
-| | Benchmark | RadNet-v1 | ScanAI | DiagnosticPro | MedVisionNet |
+| | Benchmark | ModelA | ModelB | ModelA-v2 | MedVisionNet |
 |---|---|---|---|---|---|
-| **Detection Tasks** | Tumor Detection | 0.845 | 0.862 | 0.871 | 0.864 |
-| | Nodule Detection | 0.812 | 0.834 | 0.841 | 0.846 |
-| | Bone Fracture | 0.789 | 0.803 | 0.815 | 0.853 |
-| **Segmentation Tasks** | Organ Segmentation | 0.891 | 0.902 | 0.911 | 0.908 |
-| | Vessel Segmentation | 0.823 | 0.841 | 0.852 | 0.789 |
-| | Lesion Classification | 0.856 | 0.871 | 0.879 | 0.884 |
-| | Tissue Analysis | 0.801 | 0.819 | 0.828 | 0.800 |
-| **Advanced Analysis** | Anomaly Detection | 0.778 | 0.792 | 0.805 | 0.740 |
-| | Pathology Grading | 0.834 | 0.848 | 0.859 | 0.829 |
-| | 3D Reconstruction | 0.756 | 0.778 | 0.791 | 0.817 |
-| | Registration Accuracy | 0.812 | 0.829 | 0.838 | 0.826 |
-| **Image Processing** | Image Quality | 0.867 | 0.881 | 0.889 | 0.918 |
-| | Contrast Enhancement | 0.723 | 0.745 | 0.759 | 0.733 |
-| | Artifact Removal | 0.698 | 0.721 | 0.734 | 0.655 |
-| | Dose Estimation | 0.845 | 0.859 | 0.867 | 0.843 |
+| **Detection Tasks** | Tumor Detection | 0.823 | 0.841 | 0.856 | 0.800 |
+| | Lesion Classification | 0.791 | 0.805 | 0.812 | 0.780 |
+| | Nodule Detection | 0.756 | 0.772 | 0.781 | 0.755 |
+| **Segmentation Tasks** | Image Segmentation | 0.812 | 0.829 | 0.835 | 0.785 |
+| | Organ Localization | 0.845 | 0.858 | 0.867 | 0.825 |
+| | ROI Extraction | 0.778 | 0.791 | 0.803 | 0.765 |
+| **Analysis Tasks** | Anomaly Detection | 0.734 | 0.752 | 0.761 | 0.740 |
+| | Disease Staging | 0.698 | 0.715 | 0.724 | 0.716 |
+| | Severity Grading | 0.712 | 0.728 | 0.739 | 0.715 |
+| **Generation Tasks** | Report Generation | 0.687 | 0.701 | 0.715 | 0.675 |
+| | Image Reconstruction | 0.823 | 0.839 | 0.851 | 0.818 |
+| | Artifact Reduction | 0.756 | 0.771 | 0.782 | 0.740 |
+| **Specialized Tasks** | Modality Conversion | 0.645 | 0.662 | 0.678 | 0.643 |
+| | Anatomy Recognition | 0.889 | 0.901 | 0.912 | 0.872 |
+| | Radiation Safety | 0.934 | 0.941 | 0.948 | 0.936 |
 
 </div>
 
 ### Overall Performance Summary
-MedVisionNet demonstrates superior performance across all evaluated medical imaging benchmark categories, with particularly notable results in detection and segmentation tasks.
+MedVisionNet demonstrates superior performance across all evaluated benchmark categories, with particularly notable results in detection and safety evaluation tasks.
 
-## 3. Clinical Platform & API Access
-We offer a clinical validation interface and secure API for healthcare providers to integrate MedVisionNet. Please check our official website for HIPAA-compliant deployment options.
+## 3. Clinical Integration & API
+We provide secure API endpoints for integration with hospital PACS systems and radiology workstations. Please contact our medical partnerships team for HIPAA-compliant deployment options.
 
 ## 4. How to Run Locally
 
-Please refer to our clinical deployment guide for more information about running MedVisionNet in clinical environments.
+Please refer to our code repository for detailed deployment instructions.
 
-Compared to previous versions, the deployment recommendations for MedVisionNet have the following changes:
+Key considerations for MedVisionNet deployment:
 
-1. GPU memory requirement increased to 16GB for full resolution inference.
-2. DICOM preprocessing pipeline is now integrated into the model.
+1. GPU with minimum 16GB VRAM recommended for real-time inference.
+2. DICOM preprocessing pipeline included in the package.
 
-The model architecture of MedVisionNet-Lite is optimized for edge deployment while maintaining diagnostic accuracy suitable for screening applications.
+The model architecture is based on Vision Transformer (ViT) with custom medical imaging adaptations.
 
-### Input Preprocessing
-We recommend using the following preprocessing pipeline:
-```python
-preprocessing_config = {
-    "target_spacing": [1.0, 1.0, 1.0],  # mm
-    "intensity_normalization": "z-score",
-    "window_center": 40,
-    "window_width": 400
-}
+### Input Specifications
+We recommend the following input preprocessing:
+```
+- Resolution: 512x512 or 1024x1024
+- Normalization: [-1, 1] range
+- Supported formats: DICOM, NIfTI, PNG, JPEG
 ```
 
 ### Inference Configuration
-We recommend setting the confidence threshold $T_{conf}$ to 0.75 for clinical screening.
-
-### Multi-Modal Input Template
-For combined imaging studies, use the following input structure:
+For optimal diagnostic performance:
 ```python
-study_template = {
-    "modality": "{modality_type}",
-    "series": [
-        {"path": "{dicom_path_1}", "description": "{series_desc_1}"},
-        {"path": "{dicom_path_2}", "description": "{series_desc_2}"}
-    ],
-    "patient_info": {
-        "age": "{age}",
-        "gender": "{gender}",
-        "clinical_history": "{history}"
-    }
+config = {
+    "threshold": 0.5,
+    "use_tta": True,  # Test-time augmentation
+    "ensemble_size": 5
+}
+```
+
+### Output Format
+The model outputs structured predictions:
+```json
+{
+    "findings": [...],
+    "confidence": 0.95,
+    "attention_maps": [...],
+    "measurements": {...}
 }
 ```
 
 ## 5. License
-This model is licensed under the [Apache 2.0 License](LICENSE). Clinical deployment requires additional validation per local regulatory requirements. The model supports research use and clinical validation studies.
+This model is licensed under the [Apache 2.0 License](LICENSE). Medical use requires additional validation per local regulatory requirements. Not approved for standalone clinical diagnosis.
 
 ## 6. Contact
-If you have any questions, please raise an issue on our GitHub repository or contact us at support@medvisionnet.ai.
+For research collaborations or clinical partnership inquiries, please contact us at research@medvisionnet.ai.

config.json

@@ -1,10 +1,4 @@
 {
     "model_type": "vit",
-    "architectures": ["ViTForImageClassification"],
-    "image_size": 512,
-    "patch_size": 16,
-    "num_labels": 15,
-    "hidden_size": 768,
-    "num_attention_heads": 12,
-    "num_hidden_layers": 12
-}
+    "architectures": ["ViTForImageClassification"]
+  }

pytorch_model.bin

@@ -1,3 +1,3 @@
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