# 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}")