Mustofa Ahmed’s scientific contributions

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Publications (1)

Fig. 1 Proposed methodology for automated X-ray interpretation
Fig. 2 Sample X-ray images and corresponding findings in the form of reports from the IU-Xray dataset. These reports are treated as the ground truth.
Fig. 3 Distribution of report length in number of words
Fig. 4 Report length distribution in train, test and validation split
Fig. 8 Training vs Validation Loss of ViT B16-BART Model

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Vision-Language Models for Automated Chest X-ray Interpretation: Leveraging ViT and GPT-2
  • Preprint
  • File available

January 2025

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72 Reads

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Mustofa Ahmed

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Most. Sharmin Sultana Samu

Radiology plays a pivotal role in modern medicine due to its non-invasive diagnostic capabilities. However, the manual generation of unstructured medical reports is time consuming and prone to errors. It creates a significant bottleneck in clinical workflows. Despite advancements in AI-generated radiology reports, challenges remain in achieving detailed and accurate report generation. In this study we have evaluated different combinations of multimodal models that integrate Computer Vision and Natural Language Processing to generate comprehensive radiology reports. We employed a pretrained Vision Transformer (ViT-B16) and a SWIN Transformer as the image encoders. The BART and GPT-2 models serve as the textual decoders. We used Chest X-ray images and reports from the IU-Xray dataset to evaluate the usability of the SWIN Transformer-BART, SWIN Transformer-GPT-2, ViT-B16-BART and ViT-B16-GPT-2 models for report generation. We aimed at finding the best combination among the models. The SWIN-BART model performs as the best-performing model among the four models achieving remarkable results in almost all the evaluation metrics like ROUGE, BLEU and BERTScore.

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