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Updated: Mar 29, 2026

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Fine-Tuned Segment Anything Model with Low-Rank Adaptation for Chest X-Ray Images.

Saeed S Alahmari1, Michael R Gardner2, Fawaz Alqahtani3

  • 1Department of Computer Science, Najran University, Najran 66462, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

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Fine-tuning the Segment Anything Model (SAM) with low-rank adaptation (LoRA) significantly improves chest X-ray (CXR) segmentation accuracy and efficiency compared to standard methods.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Chest X-ray (CXR) analysis is crucial for diagnosing respiratory conditions.
  • Accurate segmentation of lung structures in CXRs is essential for quantitative analysis.
  • Existing segmentation models may require extensive training data and computational resources.

Purpose of the Study:

  • To evaluate the performance of the Segment Anything Model (SAM) for CXR segmentation.
  • To investigate the effectiveness of low-rank adaptation (LoRA) for fine-tuning SAM in the medical domain.
  • To compare fine-tuned SAM with zero-shot SAM and traditional Convolutional Neural Network (CNN) models.

Main Methods:

  • Three SAM approaches were tested: zero-shot (coordinate and bounding box prompts) and fine-tuned using LoRA.
Keywords:
SAMchest X-raydeep learningsegmentation

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  • U-Net and DeepLabv3+ CNNs were trained to provide initial segmentation prompts for SAM.
  • LoRA was applied to SAM by adding lightweight adapters to Transformer blocks, freezing most parameters.

    • Main Results:

    • Fine-tuned SAM with LoRA demonstrated superior segmentation accuracy over zero-shot SAM methods.
    • The LoRA-based approach also showed improved efficiency compared to baseline CNNs.
    • Segmentation performance was evaluated on a CXR dataset including COVID-19, viral pneumonia, and normal cases.

    Conclusions:

    • Combining LoRA with SAM offers a promising strategy for efficient and accurate medical image segmentation.
    • This approach preserves SAM's pre-trained knowledge while reducing computational demands.
    • The fine-tuned SAM with LoRA shows potential for practical clinical applications in CXR analysis.