# Bio-Medical-ContactDoctorVLLM-14B-V1-102025 🏥🔬

## 🎯 Model Overview **Bio-Medical-ContactDoctorVLLM-14B-V1-102025** is a specialized vision-language model designed for comprehensive biomedical image analysis. Built on a novel architecture combining Qwen3-14B language model with Google's MedSigLIP-448 vision encoder, this model excels at analyzing diverse medical imaging modalities including X-rays, CT scans, MRI, ultrasound, histopathology, and clinical photography. ### Key Highlights - 🏗️ **Custom Biomedical Architecture**: Features a novel Perceiver Resampler bridge connecting vision and language modalities, optimized for medical imaging tasks - 🔬 **Domain-Specialized Training**: Pre-trained and fine-tuned on extensive biomedical datasets with both single and multi-image scenarios - 🗣️ **Medical Fluency**: Speaks medical terminology naturally with deep understanding of clinical contexts - 🖼️ **Multi-Image Analysis**: Native support for analyzing multiple medical images simultaneously with comparative reasoning - ⚡ **Efficient Inference**: Optimized for real-world deployment with 32 visual tokens per image ## 🏛️ Architecture ![architecture](https://huggingface.co/ContactDoctor/Bio-Medical-ContactDoctorVLLM-14B-V1-102025/resolve/main/architecture.png) ![architecture](https://huggingface.co/ContactDoctor/Bio-Medical-ContactDoctorVLLM-8B-V1-102025/resolve/main/architecture-1.png) ### Technical Specifications | Component | Details | |-----------|---------| | **Base LLM** | Qwen3-14B (40 layers, 5120 hidden size) | | **Vision Encoder** | MedSigLIP-448 (1152D) | | **Resampler** | Perceiver architecture with 8 heads, 2 layers | | **Visual Tokens** | 32 per image (compressed from 1024) | | **Total Parameters** | ~14.xB | | **Training Precision** | BFloat16 | | **Context Length** | 40,960 tokens | ## 📊 Training Data The model was trained on a diverse collection of biomedical imaging datasets: - **Radiology**: X-ray, CT, MRI across multiple anatomical regions - **Pathology**: Histopathology slides, cytology - **Clinical Imaging**: Dermatology, ophthalmology, endoscopy - **Multi-Modal Cases**: Datasets with multiple imaging studies per patient - **Report Pairs**: Image-report pairs for grounded medical reasoning Training incorporated both: - **Single-image scenarios**: Detailed analysis of individual medical images - **Multi-image scenarios**: Comparative analysis, temporal progression, multi-modal integration Note: This model is **not trained on generic images** and its responses to such inputs may be inaccurate or unreliable. ## 🚀 Quick Start ### Installation ```bash pip install torch transformers pillow ``` ### Download Model Files Ensure you have the following custom files from this repository: - `modeling_contactdoctor_vlm.py` - `configuration_contactdoctor_vlm.py` - `resampler.py` ### Single Image Analysis ```python import torch from PIL import Image from transformers import AutoTokenizer, AutoImageProcessor, TextStreamer from modeling_contactdoctor_vlm import ContactDoctorVLLM MODEL_PATH = "ContactDoctor/Bio-Medical-ContactDoctorVLLM-14B-V1-102025" IMAGE_TOKEN = "" # Load model tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) image_processor = AutoImageProcessor.from_pretrained(MODEL_PATH, trust_remote_code=True) model = ContactDoctorVLLM.from_pretrained( MODEL_PATH, torch_dtype=torch.bfloat16, device_map="auto" ) # Set image token image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN) model.set_image_token_id(image_token_id) # Prepare image image = Image.open("chest_xray.jpg").convert("RGB") pixel_values = image_processor(images=image, return_tensors="pt")["pixel_values"] # Create prompt system_prompt = """You are an expert medical image analyst. Analyze the provided medical image and provide a structured report: 1. **Modality & View**: Identify the imaging type and anatomical view 2. **Key Observations**: List significant visual findings 3. **Clinical Interpretation**: Brief assessment based on visible findings """ user_prompt = "\nProvide a detailed analysis of this medical image." messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ] # Tokenize prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True) # Generate with torch.no_grad(): outputs = model.generate( input_ids=inputs["input_ids"].to(model.device), attention_mask=inputs["attention_mask"].to(model.device), pixel_values=pixel_values.to(model.device), max_new_tokens=512, temperature=0.2, top_p=0.9, do_sample=True, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ### Multi-Image Comparative Analysis ```python # Load multiple images image_paths = ["ct_scan_1.jpg", "ct_scan_2.jpg", "mri_scan.jpg"] images = [Image.open(path).convert("RGB") for path in image_paths] pixel_values = image_processor(images=images, return_tensors="pt")["pixel_values"] # Multi-image prompt (one token per image) num_images = len(images) image_tokens = IMAGE_TOKEN * num_images user_prompt = f"{image_tokens}\nCompare these {num_images} medical images and provide an integrated analysis." messages = [ {"role": "system", "content": "You are an expert medical image analyst. Analyze multiple images and provide comparative assessment."}, {"role": "user", "content": user_prompt} ] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True) # Generate with multi-image input outputs = model.generate( input_ids=inputs["input_ids"].to(model.device), attention_mask=inputs["attention_mask"].to(model.device), pixel_values=pixel_values.to(model.device), max_new_tokens=1024, temperature=0.2, top_p=0.9, do_sample=True, ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## 💡 Use Cases ### Clinical Applications 1. **Radiology Report Generation** - Chest X-ray interpretation - CT/MRI findings summarization - Comparative analysis of serial imaging 2. **Pathology Analysis** - Histopathology slide description - Cell morphology assessment - Tissue abnormality detection 3. **Multi-Modal Integration** - Cross-modality comparison (e.g., X-ray + CT) - Temporal disease progression tracking - Pre/post-treatment assessment 4. **Medical Education** - Training case analysis - Image-based clinical reasoning - Differential diagnosis support ### Research Applications - Large-scale medical image annotation - Dataset quality assessment - Multi-image relationship extraction - Clinical study automation ## ⚙️ Generation Configuration ### Recommended Settings **For Detailed Analysis (Single Image):** ```python generation_config = { "max_new_tokens": 512, "min_new_tokens": 20, "temperature": 0.2, # Lower for factual accuracy "top_p": 0.9, "top_k": 20, "do_sample": True, "repetition_penalty": 1.15, "length_penalty": 1.0, } ``` **For Comparative Analysis (Multi-Image):** ```python generation_config = { "max_new_tokens": 1024, # Longer for comprehensive comparison "min_new_tokens": 50, "temperature": 0.3, "top_p": 0.9, "do_sample": True, "repetition_penalty": 1.15, } ``` **For Quick Screening:** ```python generation_config = { "max_new_tokens": 256, "temperature": 0.1, # More deterministic "do_sample": False, # Greedy decoding } ``` ## 📈 Performance Characteristics ### Strengths - ✅ Accurate modality and view identification - ✅ Detailed anatomical localization - ✅ Clinical terminology fluency - ✅ Multi-image reasoning and comparison - ✅ Temporal progression analysis - ✅ Structured report generation ### Limitations - ⚠️ Not a replacement for professional medical diagnosis - ⚠️ May hallucinate fine details in low-quality images - ⚠️ Limited to visual information; no access to clinical history - ⚠️ Performance varies by imaging modality and quality ## 🔒 Ethical Considerations & Disclaimer **⚠️ IMPORTANT MEDICAL DISCLAIMER ⚠️** This model is intended for **research and educational purposes only**. It is NOT approved for clinical use and should NOT be used as a substitute for professional medical advice, diagnosis, or treatment. ### Guidelines for Responsible Use 1. **Human Oversight Required**: All outputs must be reviewed by qualified healthcare professionals 2. **Not for Diagnosis**: Do not use for clinical decision-making without expert validation 3. **Bias Awareness**: Model may reflect biases in training data 4. **Privacy**: Ensure patient data is de-identified and handled per regulations (HIPAA, GDPR) 5. **Liability**: Users assume full responsibility for any application of this model ### Known Limitations - Model outputs are probabilistic and may contain errors - Cannot replace clinical judgment and expertise - May not generalize to rare conditions or novel imaging techniques - No access to patient history, lab results, or other clinical context ## 📜 Citation If you use this model in your research, please cite: ```bibtex @misc{biomedical-contactdoctor-vlm-2025, title={Bio-Medical-ContactDoctorVLLM: A Specialized Vision-Language Model for Biomedical Image Analysis}, author={ContactDoctor Team}, year={2025}, publisher={HuggingFace}, howpublished={\url{https://huggingface.co/ContactDoctor/Bio-Medical-ContactDoctorVLLM-14B-V1-102025}} } ``` ## 🤝 Contributing & Support - **Issues**: Report bugs or request features via GitHub Issues - **Discussions**: Join model discussions on HuggingFace - **Contact**: [support@contactdoctor.in] ## 📄 License This model is released under the cc-by-nc-nd-4.0 License. See LICENSE file for details. **Component Licenses:** - Base LLM (Qwen3-14B): [Qwen License] - Vision Encoder (MedSigLIP): Apache 2.0 - Custom Architecture: cc-by-nc-nd-4.0 ## 🙏 Acknowledgments - **Qwen Team** for the powerful Qwen3-14B language model - **Google Research** for MedSigLIP vision encoder - **Medical Imaging Community** for datasets and domain expertise - **CloudEXE** for GPU Sponsorship - **HuggingFace** for model hosting and infrastructure ---

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