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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2-VL-2B-Instruct |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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tags: |
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- Radiology |
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- Infer |
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- Qwen2 |
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- 2B |
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--- |
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# **Radiology-Infer-Mini** |
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Radiology-Infer-Mini is a vision-language model fine-tuned from the Qwen2-VL-2B framework, specifically designed to excel in radiological analysis, text extraction, and medical report generation. It integrates advanced multi-modal capabilities with domain-specific expertise, ensuring accurate and efficient processing of radiology-related tasks. |
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### Key Enhancements: |
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1. **State-of-the-Art Understanding of Medical Images** |
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Radiology-Infer-Mini achieves cutting-edge performance in interpreting complex medical imagery, including X-rays, MRIs, CT scans, and ultrasounds. It is fine-tuned on healthcare-specific benchmarks to ensure precise recognition of anatomical and pathological features. |
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2. **Support for Extended Medical Reports and Cases** |
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Capable of processing and analyzing extensive radiology case studies, Radiology-Infer-Mini can generate high-quality diagnostic reports and answer complex medical queries with detailed explanations. Its proficiency extends to multi-page radiology documents, ensuring comprehensive visual and textual understanding. |
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3. **Integration with Medical Devices** |
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With robust reasoning and decision-making capabilities, Radiology-Infer-Mini can seamlessly integrate with medical imaging systems and robotic platforms. It supports automated workflows for tasks such as diagnosis support, triaging, and clinical decision-making. |
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4. **Math and Diagram Interpretation** |
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Equipped with LaTeX support and advanced diagram interpretation capabilities, Radiology-Infer-Mini handles mathematical annotations, statistical data, and visual charts present in medical reports with precision. |
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5. **Multilingual Support for Medical Text** |
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Radiology-Infer-Mini supports the extraction and interpretation of multilingual texts embedded in radiological images, including English, Chinese, Arabic, Korean, Japanese, and most European languages. This feature ensures accessibility for a diverse global healthcare audience. |
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Radiology-Infer-Mini represents a transformative step in radiology-focused AI, enhancing productivity and accuracy in medical imaging and reporting. |
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### How to Use |
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```python |
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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# default: Load the model on the available device(s) |
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model = Qwen2VLForConditionalGeneration.from_pretrained( |
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"prithivMLmods/Radiology-Infer-Mini", torch_dtype="auto", device_map="auto" |
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) |
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# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. |
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# model = Qwen2VLForConditionalGeneration.from_pretrained( |
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# "prithivMLmods/Radiology-Infer-Mini", |
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# torch_dtype=torch.bfloat16, |
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# attn_implementation="flash_attention_2", |
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# device_map="auto", |
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# ) |
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# default processer |
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processor = AutoProcessor.from_pretrained("prithivMLmods/Radiology-Infer-Mini") |
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# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage. |
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# min_pixels = 256*28*28 |
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# max_pixels = 1280*28*28 |
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# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", |
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}, |
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{"type": "text", "text": "Describe this image."}, |
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], |
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} |
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] |
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# Preparation for inference |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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# Inference: Generation of the output |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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### Buf |
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```python |
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buffer = "" |
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for new_text in streamer: |
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buffer += new_text |
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# Remove <|im_end|> or similar tokens from the output |
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buffer = buffer.replace("<|im_end|>", "") |
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yield buffer |
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``` |
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### **Intended Use** |
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**Radiology-Infer-Mini** is designed to support healthcare professionals and researchers in tasks involving medical imaging and radiological analysis. Its primary applications include: |
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1. **Diagnostic Support** |
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- Analyze medical images (X-rays, MRIs, CT scans, ultrasounds) to identify abnormalities, annotate findings, and assist radiologists in forming diagnostic conclusions. |
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2. **Medical Report Generation** |
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- Automatically generate structured radiology reports from image data, reducing documentation time and improving workflow efficiency. |
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3. **Educational and Research Tools** |
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- Serve as a teaching aid for radiology students and support researchers in large-scale studies by automating image labeling and data extraction. |
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4. **Workflow Automation** |
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- Integrate with medical devices and hospital systems to automate triaging, anomaly detection, and report routing in clinical settings. |
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5. **Multi-modal Applications** |
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- Handle complex tasks involving both images and text, such as extracting patient data from images and synthesizing text-based findings with visual interpretations. |
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6. **Global Accessibility** |
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- Support multilingual radiological text understanding for use in diverse healthcare settings around the world. |
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### **Limitations** |
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While **Radiology-Infer-Mini** offers advanced capabilities, it has the following limitations: |
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1. **Medical Expertise Dependency** |
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- The model provides supplementary insights but cannot replace the expertise and judgment of a licensed radiologist or clinician. |
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2. **Data Bias** |
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- Performance may vary based on the training data, which might not fully represent all imaging modalities, patient demographics, or rare conditions. |
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3. **Edge Cases** |
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- Limited ability to handle edge cases, highly complex images, or uncommon medical scenarios that were underrepresented in its training dataset. |
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4. **Regulatory Compliance** |
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- It must be validated for compliance with local medical regulations and standards before clinical use. |
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5. **Interpretation Challenges** |
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- The model may misinterpret artifacts, noise, or low-quality images, leading to inaccurate conclusions in certain scenarios. |
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6. **Multimodal Integration** |
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- While capable of handling both visual and textual inputs, tasks requiring deep contextual understanding across different modalities might yield inconsistent results. |
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7. **Real-Time Limitations** |
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- Processing speed and accuracy might be constrained in real-time or high-throughput scenarios, especially on hardware with limited computational resources. |
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8. **Privacy and Security** |
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- Radiology-Infer-Mini must be used in secure environments to ensure the confidentiality and integrity of sensitive medical data. |
