MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation
Why this work is in the frame
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Bibliographic record
Abstract
Medical image segmentation plays a key role in healthcare, enabling accurate diagnosis and treatment planning. Vision Transformers (ViTs) show strong potential for segmentation tasks, but their dependence on large datasets limits practical usage in clinical settings. This study explores whether integrating pre-trained Large Language Models (LLMs) with ViT-based segmentation models can enhance feature refinement and improve performance in data-constrained environments. We introduce MedVisionLlama, which combines ViT encoders with pre-trained Llama weights and applies Low-Rank Adaptation (LoRA) for fine-tuning in 3D medical image segmentation. Evaluated on the Medical Segmentation Decathlon dataset, the model consistently outperformed a standard ViT, showing improved generalization across MRI and CT modalities. It maintained stable segmentation quality even with limited training data and across varied anatomical structures. Activation maps revealed sharper and more stable attention to relevant regions. Ablation studies confirmed that the performance gains stemmed from LLM-based feature refinement rather than increased model complexity. MedVisionLlama offers a scalable and data-efficient solution for medical image segmentation. Source code and implementation are available at: https://github.com/AS-Lab/Marthi-etal-2025-MedVisionLlama-Pre-Trained-LLM-Layers-to-Enhance-Medical-Image-Segmentation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it