| # 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}") |