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Automated Radiological Report Generation from Breast Ultrasound Images Using Vision and Language Transformers.

Shaheen Khatoon1, Azhar Mahmood1

  • 1Department of Computer Science and Digital Technologies, School of Architecture, Computing, and Engineering, University of East London, London E16 2RD, UK.

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Summary
This summary is machine-generated.

This study introduces a new AI system using a multimodal Transformer to automatically generate breast ultrasound reports. BioBERT-based models enhanced clinical accuracy, while GPT-2 improved report readability.

Keywords:
BioBERTGPT-2automatic report generationmultimodal learningvision transformer

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Natural Language Processing

Background:

  • Breast ultrasound is crucial for detecting abnormalities, but report generation is manual and subjective.
  • Current AI for report generation often uses older architectures, limiting their ability to understand complex medical details.
  • Automated systems can improve efficiency and consistency in radiological reporting.

Purpose of the Study:

  • To develop a novel multimodal Transformer framework for automated breast ultrasound report generation.
  • To integrate visual features from ultrasound images with textual information from language models.
  • To enhance the accuracy and fluency of AI-generated radiological reports.

Main Methods:

  • Utilized a Vision Transformer (ViT) for extracting image features from breast ultrasound scans.
  • Employed pretrained language models (BERT, BioBERT, GPT-2) for textual data processing.
  • Developed a multimodal Transformer decoder for generating reports by combining visual and textual information.

Main Results:

  • BioBERT-based models showed superior clinical specificity compared to general language models.
  • GPT-2 based decoders significantly improved the linguistic fluency of generated reports.
  • The multimodal approach effectively integrated visual and textual data for comprehensive report generation.

Conclusions:

  • The proposed Transformer-based framework offers a promising approach for automated breast ultrasound report generation.
  • Integrating domain-specific language models like BioBERT enhances clinical relevance.
  • The system has the potential to streamline radiology workflows and improve report quality.