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.
Authors
- Luping Zhou (ORCID: https://orcid.org/0000-0001-8762-2424)
Institutions
- The University of Sydney (AU)
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