Abstract
Medical imaging is widely used in clinical practice for diagnosis and treatment. Report-writing can be error-prone for unexperienced physicians, and time-consuming and tedious for experienced physicians. To address these issues, we study the automatic generation of medical imaging reports. This task presents several challenges. First, a complete report contains multiple heterogeneous forms of information, including findings and tags. Second, abnormal regions in medical images are difficult to identify. Third, the reports are typically long, containing multiple sentences. To cope with these challenges, we (1) build a multi-task learning framework which jointly performs the prediction of tags and the generation of paragraphs, (2) propose a co-attention mechanism to localize regions containing abnormalities and generate narrations for them, (3) develop a hierarchical LSTM model to generate long paragraphs. We demonstrate the effectiveness of the proposed methods on two publicly available dataset.- Anthology ID:
- P18-1240
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2577–2586
- Language:
- URL:
- https://aclanthology.org/P18-1240
- DOI:
- 10.18653/v1/P18-1240
- Cite (ACL):
- Baoyu Jing, Pengtao Xie, and Eric Xing. 2018. On the Automatic Generation of Medical Imaging Reports. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2577–2586, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- On the Automatic Generation of Medical Imaging Reports (Jing et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/P18-1240.pdf
- Code
- additional community code
- Data
- IU X-Ray