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Improving chest X-ray report generation by leveraging warm starting.

Aaron Nicolson1, Jason Dowling1, Bevan Koopman1

  • 1The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Australia.

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Warm starting deep learning models with pre-trained checkpoints significantly improves Chest X-ray (CXR) report generation accuracy. The CvT2DistilGPT2 model shows superior performance, enhancing diagnostic capabilities for clinical applications.

Keywords:
Chest X-ray report generationImage captioningMulti-modal learningWarm starting

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Natural Language Processing

Background:

  • Automated Chest X-ray (CXR) report generation aims to reduce clinical workload and enhance patient care.
  • Current encoder-to-decoder models for CXR report generation lack sufficient diagnostic accuracy for clinical deployment.

Purpose of the Study:

  • To improve the diagnostic accuracy of automated CXR report generation.
  • To investigate the effectiveness of warm-starting encoder and decoder models with pre-trained checkpoints.

Main Methods:

  • Utilized open-source computer vision checkpoints like Vision Transformer (ViT) and natural language processing checkpoints like PubMedBERT for warm-starting.
  • Evaluated checkpoint performance on the MIMIC-CXR and IU X-ray datasets.
  • Compared the proposed CvT2DistilGPT2 model against the state-of-the-art M² Transformer Progressive.

Main Results:

  • The Convolutional Vision Transformer (CvT) ImageNet-21K and Distilled Generative Pre-trained Transformer 2 (DistilGPT2) were identified as optimal checkpoints for warm-starting the encoder and decoder, respectively.
  • The CvT2DistilGPT2 model achieved significant improvements: 8.3% in CE F-1, 1.8% in BLEU-4, 1.6% in ROUGE-L, and 1.0% in METEOR compared to the M² Transformer Progressive.
  • Generated reports demonstrated higher similarity to radiologist reports.

Conclusions:

  • Warm-starting encoder and decoder models with appropriate pre-trained checkpoints substantially enhances the performance of automated CXR report generation.
  • The CvT2DistilGPT2 model represents a significant advancement in generating clinically relevant and accurate radiology reports from CXRs.