MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to
  Enhance Medical Image Segmentation
Authors: name: Gurucharan Marthi Krishna Kumar, name: Aman Chadha, name: Janine Mendola, name: Amir Shmuel

Large Language Models (LLMs), known for their versatility in textual data,
are increasingly being explored for their potential to enhance medical image
segmentation, a crucial task for accurate diagnostic imaging. This study
explores enhancing Vision Transformers (ViTs) for medical image segmentation by
integrating pre-trained LLM transformer blocks. Our approach, which
incorporates a frozen LLM transformer block into the encoder of a ViT-based
model, leads to substantial improvements in segmentation performance across
various medical imaging modalities. We propose a Hybrid Attention Mechanism
that combines global and local feature learning with a Multi-Scale Fusion Block
for aggregating features across different scales. The enhanced model shows
significant performance gains, including an average Dice score increase from
0.74 to 0.79 and improvements in accuracy, precision, and the Jaccard Index.
These results demonstrate the effectiveness of LLM-based transformers in
refining medical image segmentation, highlighting their potential to
significantly boost model accuracy and robustness. The source code and our
implementation are available at: https://bit.ly/3zf2CVs