@inproceedings{kale-etal-2022-knowledge,
title = "Knowledge Enhanced Deep Learning Model for Radiology Text Generation",
author = "Kale, Kaveri and
Bhattacharya, Pushpak and
Shetty, Aditya and
Gune, Milind and
Shrivastava, Kush and
Lawyer, Rustom and
Biswas, Spriha",
editor = "Akhtar, Md. Shad and
Chakraborty, Tanmoy",
booktitle = "Proceedings of the 19th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2022",
address = "New Delhi, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.icon-main.4",
pages = "32--42",
abstract = "Manual radiology report generation is a time-consuming task. First, radiologists prepare brief notes while carefully examining the imaging report. Then, radiologists or their secretaries create a full-text report that describes the findings by referring to the notes. Automatic radiology report generation is the primary objective of this research. The central part of automatic radiology report generation is generating the finding section (main body of the report) from the radiologists{'} notes. In this research, we suggest a knowledge graph (KG) enhanced radiology text generator that can provide additional domain-specific information. Our approach uses a KG-BART model to generate a description of clinical findings (referred to as pathological description) from radiologists{'} brief notes. We have constructed a parallel dataset of radiologists{'} notes and corresponding pathological descriptions to train the KG-BART model. Our findings demonstrate that, compared to the BART-large and T5-large models, the BLEU-2 score of the pathological descriptions generated by our approach is raised by 4{\%} and 9{\%}, and the ROUGE-L score by 2{\%} and 2{\%}, respectively. Our analysis shows that the KG-BART model for radiology text generation outperforms the T5-large model. Furthermore, we apply our proposed radiology text generator for whole radiology report generation.",
}
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<abstract>Manual radiology report generation is a time-consuming task. First, radiologists prepare brief notes while carefully examining the imaging report. Then, radiologists or their secretaries create a full-text report that describes the findings by referring to the notes. Automatic radiology report generation is the primary objective of this research. The central part of automatic radiology report generation is generating the finding section (main body of the report) from the radiologists’ notes. In this research, we suggest a knowledge graph (KG) enhanced radiology text generator that can provide additional domain-specific information. Our approach uses a KG-BART model to generate a description of clinical findings (referred to as pathological description) from radiologists’ brief notes. We have constructed a parallel dataset of radiologists’ notes and corresponding pathological descriptions to train the KG-BART model. Our findings demonstrate that, compared to the BART-large and T5-large models, the BLEU-2 score of the pathological descriptions generated by our approach is raised by 4% and 9%, and the ROUGE-L score by 2% and 2%, respectively. Our analysis shows that the KG-BART model for radiology text generation outperforms the T5-large model. Furthermore, we apply our proposed radiology text generator for whole radiology report generation.</abstract>
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%0 Conference Proceedings
%T Knowledge Enhanced Deep Learning Model for Radiology Text Generation
%A Kale, Kaveri
%A Bhattacharya, Pushpak
%A Shetty, Aditya
%A Gune, Milind
%A Shrivastava, Kush
%A Lawyer, Rustom
%A Biswas, Spriha
%Y Akhtar, Md. Shad
%Y Chakraborty, Tanmoy
%S Proceedings of the 19th International Conference on Natural Language Processing (ICON)
%D 2022
%8 December
%I Association for Computational Linguistics
%C New Delhi, India
%F kale-etal-2022-knowledge
%X Manual radiology report generation is a time-consuming task. First, radiologists prepare brief notes while carefully examining the imaging report. Then, radiologists or their secretaries create a full-text report that describes the findings by referring to the notes. Automatic radiology report generation is the primary objective of this research. The central part of automatic radiology report generation is generating the finding section (main body of the report) from the radiologists’ notes. In this research, we suggest a knowledge graph (KG) enhanced radiology text generator that can provide additional domain-specific information. Our approach uses a KG-BART model to generate a description of clinical findings (referred to as pathological description) from radiologists’ brief notes. We have constructed a parallel dataset of radiologists’ notes and corresponding pathological descriptions to train the KG-BART model. Our findings demonstrate that, compared to the BART-large and T5-large models, the BLEU-2 score of the pathological descriptions generated by our approach is raised by 4% and 9%, and the ROUGE-L score by 2% and 2%, respectively. Our analysis shows that the KG-BART model for radiology text generation outperforms the T5-large model. Furthermore, we apply our proposed radiology text generator for whole radiology report generation.
%U https://aclanthology.org/2022.icon-main.4
%P 32-42
Markdown (Informal)
[Knowledge Enhanced Deep Learning Model for Radiology Text Generation](https://aclanthology.org/2022.icon-main.4) (Kale et al., ICON 2022)
ACL
- Kaveri Kale, Pushpak Bhattacharya, Aditya Shetty, Milind Gune, Kush Shrivastava, Rustom Lawyer, and Spriha Biswas. 2022. Knowledge Enhanced Deep Learning Model for Radiology Text Generation. In Proceedings of the 19th International Conference on Natural Language Processing (ICON), pages 32–42, New Delhi, India. Association for Computational Linguistics.