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Journals / Artificial Intelligence Theory and Applications / 2025 / Volume: 5 Issue: 2

Report Generation from X-ray Images: An Evaluation with Transformer Architectures

Bengü Fetiler(CELAL BAYAR UNIVERSITY)0000-0002-2761-7751
Ömer Atılım Koca(IZMIR BAKIRCAY UNIVERSITY)0009-0007-7286-6785
Volkan Kılıç(IZMIR KATIP CELEBI UNIVERSITY)0000-0002-3164-1981
Pages
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Publication Date
2025-10-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Their ability to capture radiological findings and generate clinically meaningful reports is evaluated through comparative analyses conducted under diverse experimental configurations on IU X-RAY dataset.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The automatic generation of medical reports from chest X-ray images has attached increasing attention due to its capability to enhance diagnostic accuracy and reduce work-load in clinical decision support.
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Key Findings & Data:The latest advancements in medical report generation, particularly with encoder-decoder models, emphasize their ability to integrate visual information with textual reports.
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Conclusion & Impact:Their ability to capture radiological findings and generate clinically meaningful reports is evaluated through comparative analyses conducted under diverse experimental configurations on IU X-RAY dataset.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:convolutional vision transformer · image processing · medical report generation

Abstract

The automatic generation of medical reports from chest X-ray images has attached increasing attention due to its capability to enhance diagnostic accuracy and reduce work-load in clinical decision support. The latest advancements in medical report generation, particularly with encoder-decoder models, emphasize their ability to integrate visual information with textual reports. However, these models suffer from challenges such as generating generic statements, failing to capture detailed pathological findings, and inconsistency of reports. In this study, the effectiveness of Vision Transformer and Convolutional Vision Transformer encoders combined with GPT2-based (Generative Pre-trained Transformer) decoders are investigated for the task of chest X-ray report generation. Their ability to capture radiological findings and generate clinically meaningful reports is evaluated through comparative analyses conducted under diverse experimental configurations on IU X-RAY dataset.

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Cite This Article

Fetiler, B., Koca, Ö. A., Kılıç, V. (2025). Report Generation from X-ray Images: An Evaluation with Transformer Architectures. Artificial Intelligence Theory and Applications, Volume: 5 Issue: 2. https://ajindex.com/articles/1609591

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