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:

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}")