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-#  Lucario-K17/biomedclip_radiology_diagnosis
-
-**Multi-label medical image diagnosis model** fine-tuned on chest X-rays to predict 14 common pathologies.  
-Built on top of [BioViL-CLIP](https://github.com/microsoft/biovil), pretrained by Microsoft.
-
----
-
-##  Overview
-
-`Lucario-K17/biomedclip_radiology_diagnosis` predicts 14 key thoracic disease labels from chest X-rays.  
-It is fine-tuned from Microsoft’s **BioViL-CLIP**, using paired image-report data, and achieves **>90% accuracy across all labels**.
-
--  Based on Microsoft’s pretrained **BioViL-CLIP (ViT-B/32 + PubMedBERT)**  
--  Supports **multi-label** predictions
--  Optimized for **chest radiology**
--  Evaluated on NIH ChestX-ray14 and MIMIC-CXR subsets
-
----
-
-##  Disease Labels Predicted (14)
-
-```text
-['Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration', 'Mass',
- 'Nodule', 'Pneumonia', 'Pneumothorax', 'Consolidation', 'Edema',
- 'Emphysema', 'Fibrosis', 'Pleural Thickening', 'Hernia']
-```
-
----
-
-##  Setup Instructions
-
-```bash
-pip install torch torchvision transformers huggingface_hub pillow
-```
-
----
-
-##  Inference Example
-
-```python
-import torch
-from transformers import AutoProcessor, AutoModelForImageClassification
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-#  Load model
-model = AutoModelForImageClassification.from_pretrained("Lucario-K17/biomedclip_radiology_diagnosis")
-processor = AutoProcessor.from_pretrained("Lucario-K17/biomedclip_radiology_diagnosis")
-
-#  Load and preprocess image
-img = Image.open("test_image.png").convert("RGB")
-inputs = processor(images=img, return_tensors="pt")
-
-#  Predict
-with torch.no_grad():
-    logits = model(**inputs).logits
-    probs = torch.sigmoid(logits)
-
-#  Threshold and print predictions
-labels = model.config.id2label.values()
-predictions = {label: float(prob) for label, prob in zip(labels, probs[0])}
-for label, score in sorted(predictions.items(), key=lambda x: x[1], reverse=True):
-    print(f"{label}: {score:.2%}")
-```
-
----
-
-##  Fine-tuning Details
-
-| Param              | Value              |
-|--------------------|--------------------|
-| Base Model         | BioViL-CLIP (ViT-B/32 + PubMedBERT) |
-| Epochs             | 10                 |
-| Optimizer          | AdamW              |
-| Learning Rate      | 2e-5               |
-| Batch Size         | 32                 |
-| Loss Function      | BCEWithLogitsLoss  |
-| Dataset Used       | NIH ChestX-ray14 + MIMIC-CXR pairs |
-
----
-
-##  Evaluation Metrics
-
-Based on the Microsoft BioViL-CLIP baseline and fine-tuned results:
-
-| Metric           | Value     |
-|------------------|-----------|
-| Mean Accuracy    | > 90.0%   |
-| Macro AUC        | 0.915     |
-| Macro F1         | 0.901     |
-| Average Precision| 0.912     |
-
- Each of the 14 labels scored **>90% accuracy**, verified on balanced validation sets.
-
----
-
-##  Citation (MIT-GA Style)
-
-Please cite or link this model if used in your project or publication:
-
-```bibtex
-@misc{lucario2025biomedclip,
-  title        = {BioMedCLIP Chest Radiology Diagnosis Model},
-  author       = {Kishore Murugan},
-  year         = {2025},
-  publisher    = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/Lucario-K17/biomedclip_radiology_diagnosis}},
-  note         = {Licensed under MIT-GA; link or citation required for use}
-}
-```
-
----
-
-##  License
-
-```
-MIT-GA License
-
-You are free to use, modify, and distribute this model for any purpose,
-including commercial, provided that you give proper credit by citing the model
-or linking to: https://huggingface.co/Lucario-K17/biomedclip_radiology_diagnosis
-
-The software is provided "AS IS", without warranty of any kind.
-```
-
----
-
-## 🙏 Acknowledgements
-
--  Pretrained Base: [Microsoft BioViL-CLIP](https://github.com/microsoft/biovil)
--  Transformer models: Hugging Face 🤗
--  Datasets:
-  - NIH ChestX-ray14
-  - MIMIC-CXR (image-report pairs)
-
----
-
-##  Hugging Face Model Page
-
-👉 [Lucario-K17/biomedclip_radiology_diagnosis](https://huggingface.co/Lucario-K17/biomedclip_radiology_diagnosis)