Upload MedVisionNet-ClinicalRelease model (epoch 500 - best eval_accuracy)

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-library_name: transformers
+library_name: monai
 ---
 # MedVisionNet
 <!-- markdownlint-disable first-line-h1 -->
@@ -20,15 +20,15 @@ library_name: transformers
 
 ## 1. Introduction
 
-MedVisionNet represents a breakthrough in medical imaging AI. In this latest release, MedVisionNet has dramatically enhanced its diagnostic accuracy through advanced transfer learning and domain-specific fine-tuning. The model excels across diverse medical imaging modalities including X-rays, CT scans, MRIs, and ultrasound imaging.
+MedVisionNet represents a breakthrough in medical imaging AI. This deep learning model has been specifically designed for multi-modal medical image analysis, supporting CT, MRI, X-ray, and ultrasound imaging modalities. The model leverages transformer-based attention mechanisms combined with convolutional backbones to achieve state-of-the-art performance across clinical diagnostic tasks.
 
 <p align="center">
   <img width="80%" src="figures/fig3.png">
 </p>
 
-Compared to the previous version, the upgraded model demonstrates substantial improvements in detecting subtle pathologies. For example, in the ChestX-ray14 benchmark, the model's sensitivity increased from 82% in the previous version to 94% in the current version. This advancement results from deeper feature extraction: the previous model used an average of 256 feature maps, while the new version utilizes 512 feature maps per layer.
+In clinical validation studies, MedVisionNet demonstrated exceptional performance across multiple diagnostic categories. The model achieved a sensitivity of 94.2% for early-stage tumor detection, significantly outperforming radiologist baseline performance of 87.3%. For organ segmentation tasks, the model achieved a Dice coefficient improvement from 0.82 to 0.91 compared to previous versions.
 
-Beyond improved detection capabilities, this version offers enhanced explainability through attention mapping and reduced false-positive rates in screening applications.
+The architecture incorporates FDA-compliant uncertainty quantification, providing confidence scores that assist clinicians in decision-making while maintaining regulatory compliance for medical device software.
 
 ## 2. Evaluation Results
 
@@ -36,78 +36,86 @@ Beyond improved detection capabilities, this version offers enhanced explainabil
 
 <div align="center">
 
-| | Benchmark | RadNet-1 | DiagAI | MedScan-v2 | MedVisionNet |
+| | Benchmark | RadNet-v1 | ClinicalAI | DeepMed-2 | MedVisionNet |
 |---|---|---|---|---|---|
-| **Primary Diagnostics** | Tumor Detection | 0.812 | 0.835 | 0.841 | 0.779 |
-| | Organ Segmentation | 0.789 | 0.801 | 0.815 | 0.800 |
-| | Fracture Detection | 0.756 | 0.772 | 0.785 | 0.821 |
-| **Specialized Screening** | Retinal Screening | 0.871 | 0.885 | 0.890 | 0.884 |
-| | Chest X-ray Classification | 0.782 | 0.799 | 0.811 | 0.750 |
-| | Skin Lesion Analysis | 0.803 | 0.821 | 0.830 | 0.839 |
-| | Brain MRI Analysis | 0.767 | 0.781 | 0.799 | 0.771 |
-| **Advanced Imaging** | Cardiac Imaging | 0.715 | 0.731 | 0.748 | 0.721 |
-| | Mammography Screening | 0.788 | 0.805 | 0.812 | 0.752 |
-| | CT Scan Interpretation | 0.721 | 0.739 | 0.755 | 0.806 |
-| | Pathology Classification | 0.845 | 0.865 | 0.870 | 0.892 |
-| **Anatomical Analysis**| Ultrasound Analysis | 0.682 | 0.699 | 0.715 | 0.696 |
-| | Bone Density Assessment | 0.751 | 0.768 | 0.780 | 0.681 |
-| | Lesion Localization | 0.733 | 0.749 | 0.761 | 0.755 |
-| | Anatomical Landmark Detection | 0.818 | 0.831 | 0.845 | 0.794 |
+| **Core Diagnostic Tasks** | Tumor Detection | 0.821 | 0.835 | 0.842 | 0.890 |
+| | Organ Segmentation | 0.765 | 0.781 | 0.790 | 0.836 |
+| | Fracture Detection | 0.889 | 0.901 | 0.912 | 0.940 |
+| **Classification Tasks** | Lesion Classification | 0.723 | 0.739 | 0.752 | 0.728 |
+| | Nodule Detection | 0.812 | 0.825 | 0.831 | 0.896 |
+| | Tissue Analysis | 0.698 | 0.711 | 0.725 | 0.759 |
+| | Anomaly Detection | 0.756 | 0.768 | 0.779 | 0.777 |
+| **Advanced Analytics** | Vessel Tracking | 0.687 | 0.695 | 0.708 | 0.744 |
+| | Disease Staging | 0.734 | 0.748 | 0.761 | 0.802 |
+| | Image Registration | 0.812 | 0.823 | 0.835 | 0.919 |
+| | Dose Prediction | 0.651 | 0.667 | 0.679 | 0.687 |
+| **Clinical Outcomes** | Survival Analysis | 0.723 | 0.735 | 0.748 | 0.805 |
+| | Treatment Response | 0.689 | 0.702 | 0.715 | 0.689 |
+| | Biomarker Extraction | 0.778 | 0.791 | 0.803 | 0.822 |
+| | Safety Compliance | 0.912 | 0.921 | 0.928 | 0.894 |
 
 </div>
 
 ### Overall Performance Summary
-MedVisionNet demonstrates state-of-the-art performance across all evaluated medical imaging benchmark categories, with particularly strong results in tumor detection and pathology classification.
+MedVisionNet demonstrates strong performance across all evaluated clinical benchmark categories, with particularly notable results in diagnostic accuracy and FDA safety compliance metrics.
 
-## 3. Clinical API & Web Interface
-We offer a HIPAA-compliant API and web interface for clinical integration with MedVisionNet. Please contact our healthcare partnerships team for deployment details.
+## 3. Clinical Portal & API Platform
+We offer a HIPAA-compliant clinical portal and REST API for healthcare institutions to integrate MedVisionNet. Please contact our enterprise team for deployment options.
 
 ## 4. How to Run Locally
 
-Please refer to our clinical documentation repository for detailed deployment instructions.
+Please refer to our code repository for information about deploying MedVisionNet in your clinical environment.
 
-Compared to previous versions, the deployment recommendations for MedVisionNet have the following changes:
+Key deployment considerations for MedVisionNet:
 
-1. DICOM format is now natively supported.
-2. Multi-GPU inference is enabled by default for batch processing.
+1. DICOM integration is supported via PyDICOM.
+2. GPU acceleration requires CUDA 11.8+ for optimal inference speed.
 
-The model architecture of MedVisionNet-Lite is a distilled version suitable for edge deployment.
+The model architecture follows the MONAI framework conventions and can be loaded using standard PyTorch methods.
 
-### Preprocessing Requirements
-We recommend using the following preprocessing pipeline:
+### System Requirements
+We recommend the following hardware specifications for clinical deployment:
 ```
-Standard medical image normalization with HU windowing for CT scans.
-Image size: 512x512 pixels minimum resolution.
+GPU: NVIDIA A100 or V100 (40GB VRAM minimum)
+RAM: 64GB minimum
+Storage: 500GB SSD for model caching
+```
+For example deployment configuration:
+```
+docker run --gpus all -p 8080:8080 medvisionnet:latest
 ```
 
-### Inference Parameters
-We recommend setting the confidence threshold to 0.7 for screening applications.
+### Temperature
+For diagnostic predictions, we recommend temperature scaling with $T_{model}$ = 0.8 for optimal calibration.
 
-### Input Format for DICOM Processing
-For DICOM file processing, please follow this template where {patient_id}, {study_uid}, and {series_uid} are arguments:
+### Input Preprocessing
+For DICOM input, please follow the standardized preprocessing pipeline:
 ```
-dicom_template = \
-"""[patient_id]: {patient_id}
-[study_uid]: {study_uid}
-[series_uid]: {series_uid}
-"""
+preprocessing_config = \
+"""[modality]: {modality_type}
+[window_center]: {wc}
+[window_width]: {ww}
+[normalization]: hounsfield_to_unit
+{input_path}"""
 ```
 
-For batch processing with anonymization enabled, we recommend the following configuration:
+For multi-modal fusion inference, we recommend the following configuration where {ct_path}, {mri_path}, and {clinical_notes} are arguments:
 ```
-batch_config = \
-'''# Batch Processing Configuration
-{study_list}
-Processing parameters:
-- Anonymization: Enabled
-- Date: {processing_date}
-- Output format: {output_format}
-'''
+fusion_template = \
+'''# Multi-modal Medical Image Fusion Pipeline
+{ct_dicom_path}
+{mri_dicom_path}
+Integration of imaging data with clinical context requires proper alignment of spatial coordinates. Each input modality should be registered to a common anatomical reference frame before fusion.
+Key processing considerations:
+- Apply histogram matching for intensity normalization.
+- Use affine registration for gross alignment.
+- Apply deformable registration for local corrections.
+- Clinical notes provide context for diagnostic reasoning.
+{clinical_notes}'''
 ```
 
 ## 5. License
-This code repository is licensed under the [Apache-2.0 License](LICENSE). The use of MedVisionNet models is subject to healthcare regulatory compliance requirements.
+This model is released under the [Apache 2.0 License](LICENSE). Commercial use requires validation study completion and regulatory clearance in your jurisdiction.
 
 ## 6. Contact
-If you have any questions, please raise an issue on our GitHub repository or contact us at clinical@medvisionnet.ai.
-```
+For clinical inquiries, please contact our medical affairs team at clinical@medvisionnet.ai or submit a support ticket through our enterprise portal.

config.json

@@ -1,10 +1,8 @@
 {
-    "model_type": "vit",
-    "architectures": ["ViTForImageClassification"],
-    "image_size": 512,
-    "patch_size": 16,
-    "num_channels": 1,
+    "model_type": "vision_transformer",
+    "architectures": ["MedVisionNet"],
+    "input_channels": 1,
+    "output_classes": 15,
     "hidden_size": 768,
-    "num_attention_heads": 12,
-    "medical_domain": "radiology"
+    "num_attention_heads": 12
 }

pytorch_model.bin

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