Bibliographic Information: Marthi Krishna Kumar, Gurucharan; Chadha, Aman; Mendola, Janine; Shmuel, Amir (2024). MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation. Submitted to IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025. arXiv:2410.02458v1 [eess.IV].
Research Objective: This research paper investigates the effectiveness of incorporating pre-trained LLM transformer blocks into ViT models to enhance medical image segmentation performance.
Methodology: The researchers developed MedVisionLlama, a novel architecture that integrates a frozen transformer block from a pre-trained LLM (e.g., Llama 3.1) into the encoder of a ViT. They employed a hybrid attention mechanism combining efficient and channel attention for balanced feature learning and a multi-scale fusion block for aggregating features across scales. The model was evaluated on the ten datasets of the Medical Segmentation Decathlon (MSD) challenge and compared against a baseline ViT model using metrics like Dice score, accuracy, precision, recall, Jaccard Index, and HD95. Ablation studies were conducted to assess the impact of different LLM architectures and model components.
Key Findings: Integrating the Llama 3.1 transformer block significantly improved segmentation performance across all MSD tasks, with notable increases in Dice score, accuracy, and other metrics. The hybrid attention mechanism and multi-scale fusion block further contributed to performance gains. Ablation studies confirmed the effectiveness of the LLM integration and highlighted the superior performance of lighter LLMs like Qwen and Yi in this context.
Main Conclusions: The study demonstrates that pre-trained LLM transformer blocks, even when frozen, can serve as powerful enhancers for medical image segmentation tasks. This approach eliminates the need for extensive labeled datasets and computational resources typically required for training ViTs from scratch. The authors suggest that lighter LLMs offer a good balance between efficiency and performance for this application.
Significance: This research contributes to the growing field of applying LLMs to computer vision tasks, particularly in the crucial domain of medical image analysis. The findings have the potential to improve the accuracy and efficiency of medical image segmentation, ultimately benefiting diagnosis and treatment planning.
Limitations and Future Research: The study primarily focuses on segmentation performance and does not extensively explore the generalizability of the approach to other medical imaging tasks. Future research could investigate the application of this method to different modalities, tasks, and LLM architectures. Additionally, exploring the impact of fine-tuning the LLM layers could yield further insights.
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by Gurucharan M... : arxiv.org 10-04-2024
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