Create README.md

README.md

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+# medcaption-vif-clip
+
+## Model Overview
+
+The `medcaption-vif-clip` model is a **Vision-Language Model (VLM)** designed specifically for **Medical Image Captioning**. It takes a medical scan image (e.g., X-ray, MRI, CT) as input and generates a descriptive, clinically relevant natural language caption/summary. This model utilizes a Vision-Encoder-Decoder architecture for robust image-to-text generation.
+
+## Model Architecture
+
+* **Architecture:** **Vision-Encoder-Decoder Model** (similar to ImageGPT/CLIP-GPT fusion).
+    * **Vision Encoder:** A frozen **CLIP ViT-Base** variant, fine-tuned to extract visual features from medical images. 
+    * **Language Decoder:** A specialized, smaller **GPT-2** decoder, conditioned on the output of the Vision Encoder, generating the descriptive text.
+* **Mechanism:** The encoder processes the image, and its final hidden state is used to initialize the decoder's sequence generation process, ensuring the text is grounded in the visual evidence.
+
+## Intended Use
+
+* **Radiology Workflow:** Automating the first draft of image findings to increase radiologist efficiency.
+* **Medical Education:** Generating explanations for complex anatomical features or pathology in image libraries.
+* **Search and Indexing:** Creating searchable text descriptions for large archives of unlabeled medical scans.
+
+## Limitations and Ethical Considerations
+
+* **Safety Criticality:** **This model must NOT be used for primary diagnosis.** It is an automated tool and can generate inaccurate, incomplete, or confusing captions that could lead to misdiagnosis. All outputs require human expert validation.
+* **Generalization:** Trained mainly on chest X-rays and basic CTs. Performance may degrade severely on highly specialized or rare scan types (e.g., PET scans, functional MRI).
+* **Sensitive Content:** Dealing with medical imagery is inherently sensitive. Data protection and ethical handling of all input and output are paramount.
+* **Visual Ambiguity:** The model cannot report findings that are visually ambiguous or require comparison with a prior scan (longitudinal assessment), which a human radiologist would perform.
+
+## Example Code
+
+To generate a caption for a medical image:
+
+```python
+from transformers import VisionEncoderDecoderModel, AutoTokenizer, AutoFeatureExtractor
+from PIL import Image
+import torch
+
+# Load model, tokenizer (for the decoder), and feature extractor (for the encoder)
+model_name = "YourOrg/medcaption-vif-clip"
+model = VisionEncoderDecoderModel.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained("gpt2")
+feature_extractor = AutoFeatureExtractor.from_pretrained("clip-vit-base-patch16")
+
+# Set up generation parameters
+model.config.eos_token_id = tokenizer.eos_token_id
+model.config.decoder_start_token_id = tokenizer.bos_token_id
+
+# 1. Load the Image (Conceptual - Replace with actual image loading)
+# Example: X-ray of a chest
+dummy_image = Image.new('RGB', (224, 224), color = 'gray') 
+
+# 2. Preprocess the image
+pixel_values = feature_extractor(images=dummy_image, return_tensors="pt").pixel_values
+
+# 3. Generate the caption
+generated_ids = model.generate(pixel_values, max_length=50, num_beams=4)
+
+# 4. Decode the text
+caption = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+print(f"Generated Medical Caption: {caption}")