Automated Medical Report Generation and Visual Question Answering

The rapid growth of medical imaging data has far outpaced the availability of trained radiologists, significantly increasing their workload. To alleviate this burden, reduce diagnostic errors, and streamline clinical workflows, the need for automated medical diagnostic report generation has become more urgent than ever. However, this task is particularly challenging, as it requires the ability to capture and describe clinically significant fine-grained visual differences in highly similar medical images. Additionally, critical disease-related keywords can easily be overshadowed by the prevalence of similar phrases describing common image content. Moreover, generating comprehensive reports that detail both normal and pathological findings within images adds to the complexity.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1145/3688868.3689189
Citations
1
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.25
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article

Automated Medical Report Generation and Visual Question Answering

Luping Zhou
1 citations
Multimodal Machine Learning Applications
0.25
article

Automated Medical Report Generation and Visual Question Answering

Luping Zhou
article en
1 citations

Abstract

The rapid growth of medical imaging data has far outpaced the availability of trained radiologists, significantly increasing their workload. To alleviate this burden, reduce diagnostic errors, and streamline clinical workflows, the need for automated medical diagnostic report generation has become more urgent than ever. However, this task is particularly challenging, as it requires the ability to capture and describe clinically significant fine-grained visual differences in highly similar medical images. Additionally, critical disease-related keywords can easily be overshadowed by the prevalence of similar phrases describing common image content. Moreover, generating comprehensive reports that detail both normal and pathological findings within images adds to the complexity.

The University of Sydney (AU)
Openalex Percentile: Top 45%
Multimodal Machine Learning Applications
0.25
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