MedVisionLlama: Doctors without Segmented Borders
ViT + LLM is a great combo for medical images
Paper: MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation (15 Pages)
Researchers from McGill University and Stanford are interested in enhanced medical image segmentation.
Hmm..What’s the background?
Medical image segmentation plays a crucial role in healthcare by identifying and outlining anatomical structures or abnormalities in medical images (e.g., MRI, X-rays) to assist in diagnosis and treatment planning. Vision Transformers (ViTs), inspired by the success of transformers in natural language processing, have emerged as powerful tools for image segmentation due to their ability to capture long-range spatial relationships within images. However, ViTs often require large, expertly labeled datasets, which are expensive and time-consuming to obtain in the medical field.
Ok, So what is proposed in the research paper?
The paper presents:
This approach involves taking a pre-trained LLM (like Llama, Gemma, Mistral, Qwen, or Yi) and inserting a frozen transformer block from this LLM into the encoder part of a ViT model
The addition of the LLM component leads to better Dice scores, precision, Jaccard Index, and Hausdorff Distance (HD95) values, indicating more accurate segmentation results and better boundary delineation
While larger LLMs like Gemma, Llama, and Mistral offer high representational capacity, lighter models like Qwen and Yi have shown surprisingly strong performance, often outperforming heavier counterparts. This suggests a good balance between efficiency and accuracy for specific medical image segmentation tasks.
What’s next?
Further research is needed to investigate the optimal LLM architectures, sizes, and integration strategies for specific medical image segmentation tasks. As with any AI application in healthcare, careful consideration must be given to ethical implications, data privacy, and potential biases to ensure responsible development and deployment. Further research and development in this area have the potential to revolutionize medical image analysis, ultimately leading to improved diagnosis, treatment, and patient care.
So essentially,
ViT + LLM is a great combo for medical images
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