MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929614974091264 |
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| author | Asiimwe, Arnold Caleb Surís, Dídac Rajpurkar, Pranav Vondrick, Carl |
| author_facet | Asiimwe, Arnold Caleb Surís, Dídac Rajpurkar, Pranav Vondrick, Carl |
| contents | In medical reporting, the accuracy of radiological reports, whether generated by humans or machine learning algorithms, is critical. We tackle a new task in this paper: image-conditioned autocorrection of inaccuracies within these reports. Using the MIMIC-CXR dataset, we first intentionally introduce a diverse range of errors into reports. Subsequently, we propose a two-stage framework capable of pinpointing these errors and then making corrections, simulating an \textit{autocorrection} process. This method aims to address the shortcomings of existing automated medical reporting systems, like factual errors and incorrect conclusions, enhancing report reliability in vital healthcare applications. Importantly, our approach could serve as a guardrail, ensuring the accuracy and trustworthiness of automated report generation. Experiments on established datasets and state of the art report generation models validate this method's potential in correcting medical reporting errors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02971 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting Asiimwe, Arnold Caleb Surís, Dídac Rajpurkar, Pranav Vondrick, Carl Computer Vision and Pattern Recognition In medical reporting, the accuracy of radiological reports, whether generated by humans or machine learning algorithms, is critical. We tackle a new task in this paper: image-conditioned autocorrection of inaccuracies within these reports. Using the MIMIC-CXR dataset, we first intentionally introduce a diverse range of errors into reports. Subsequently, we propose a two-stage framework capable of pinpointing these errors and then making corrections, simulating an \textit{autocorrection} process. This method aims to address the shortcomings of existing automated medical reporting systems, like factual errors and incorrect conclusions, enhancing report reliability in vital healthcare applications. Importantly, our approach could serve as a guardrail, ensuring the accuracy and trustworthiness of automated report generation. Experiments on established datasets and state of the art report generation models validate this method's potential in correcting medical reporting errors. |
| title | MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.02971 |