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Model: prithivMLmods/Radiology-Infer-Mini Source: Original Platform
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README.md
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README.md
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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.
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