Pragmatic Radiology Report Generation

Dang Nguyen, Chacha Chen, He He, Chenhao Tan
Proceedings of the 3rd Machine Learning for Health Symposium, PMLR 225:385-402, 2023.

Abstract

When pneumonia is not found on a chest X-ray, should the report describe this negative observation or omit it? We argue that this question cannot be answered from the X-ray alone and requires a pragmatic perspective, which captures the communicative goal that radiology reports serve between radiologists and patients. However, the standard image-to-text formulation for radiology report generation fails to incorporate such pragmatic intents. Following this pragmatic perspective, we demonstrate that the indication, which describes why a patient comes for an X-ray, drives the mentions of negative observations. We thus introduce indications as additional input to report generation. With respect to the output, we develop a framework to identify uninferable information from the image, which could be a source of model hallucinations, and limit them by cleaning groundtruth reports. Finally, we use indications and cleaned groundtruth reports to develop pragmatic models, and show that they outperform existing methods not only in new pragmatics-inspired metrics (e.g., +4.3 Negative F1) but also in standard metrics (e.g., +6.3 Positive F1 and +11.0 BLEU-2).

Cite this Paper

BibTeX
@InProceedings{pmlr-v225-nguyen23a, title = {Pragmatic Radiology Report Generation}, author = {Nguyen, Dang and Chen, Chacha and He, He and Tan, Chenhao}, booktitle = {Proceedings of the 3rd Machine Learning for Health Symposium}, pages = {385--402}, year = {2023}, editor = {Hegselmann, Stefan and Parziale, Antonio and Shanmugam, Divya and Tang, Shengpu and Asiedu, Mercy Nyamewaa and Chang, Serina and Hartvigsen, Tom and Singh, Harvineet}, volume = {225}, series = {Proceedings of Machine Learning Research}, month = {10 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v225/nguyen23a/nguyen23a.pdf}, url = {https://proceedings.mlr.press/v225/nguyen23a.html}, abstract = {When pneumonia is not found on a chest X-ray, should the report describe this negative observation or omit it? We argue that this question cannot be answered from the X-ray alone and requires a pragmatic perspective, which captures the communicative goal that radiology reports serve between radiologists and patients. However, the standard image-to-text formulation for radiology report generation fails to incorporate such pragmatic intents. Following this pragmatic perspective, we demonstrate that the indication, which describes why a patient comes for an X-ray, drives the mentions of negative observations. We thus introduce indications as additional input to report generation. With respect to the output, we develop a framework to identify uninferable information from the image, which could be a source of model hallucinations, and limit them by cleaning groundtruth reports. Finally, we use indications and cleaned groundtruth reports to develop pragmatic models, and show that they outperform existing methods not only in new pragmatics-inspired metrics (e.g., +4.3 Negative F1) but also in standard metrics (e.g., +6.3 Positive F1 and +11.0 BLEU-2).} }
Endnote
%0 Conference Paper %T Pragmatic Radiology Report Generation %A Dang Nguyen %A Chacha Chen %A He He %A Chenhao Tan %B Proceedings of the 3rd Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2023 %E Stefan Hegselmann %E Antonio Parziale %E Divya Shanmugam %E Shengpu Tang %E Mercy Nyamewaa Asiedu %E Serina Chang %E Tom Hartvigsen %E Harvineet Singh %F pmlr-v225-nguyen23a %I PMLR %P 385--402 %U https://proceedings.mlr.press/v225/nguyen23a.html %V 225 %X When pneumonia is not found on a chest X-ray, should the report describe this negative observation or omit it? We argue that this question cannot be answered from the X-ray alone and requires a pragmatic perspective, which captures the communicative goal that radiology reports serve between radiologists and patients. However, the standard image-to-text formulation for radiology report generation fails to incorporate such pragmatic intents. Following this pragmatic perspective, we demonstrate that the indication, which describes why a patient comes for an X-ray, drives the mentions of negative observations. We thus introduce indications as additional input to report generation. With respect to the output, we develop a framework to identify uninferable information from the image, which could be a source of model hallucinations, and limit them by cleaning groundtruth reports. Finally, we use indications and cleaned groundtruth reports to develop pragmatic models, and show that they outperform existing methods not only in new pragmatics-inspired metrics (e.g., +4.3 Negative F1) but also in standard metrics (e.g., +6.3 Positive F1 and +11.0 BLEU-2).
APA
Nguyen, D., Chen, C., He, H. & Tan, C.. (2023). Pragmatic Radiology Report Generation. Proceedings of the 3rd Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 225:385-402 Available from https://proceedings.mlr.press/v225/nguyen23a.html.

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