CID at RRG24: Attempting in a conditionally initiated decoding of Radiology Report Generation with clinical entities

Liao, Yuxiang, Liang, Yuanbang, Qin, Yipeng, Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 and Spasic, Irena ORCID: https://orcid.org/0000-0002-8132-3885 2024. CID at RRG24: Attempting in a conditionally initiated decoding of Radiology Report Generation with clinical entities. Presented at: The 23rd Workshop on Biomedical Natural Language Processing, Bangkok, Thailand, 16 August 2024. Published in: Demner-Fushman, Dina, Ananiadou, Sophia, Miwa, Makoto, Roberts, Kirk and Tsujii, Junichi eds. Proceedings of the 23rd Workshop on Biomedical Natural Language Processing. Association for Computational Linguistics, pp. 591-596. 10.18653/v1/2024.bionlp-1.49

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Abstract

Radiology Report Generation (RRG) seeks to leverage deep learning techniques to automate the reporting process of radiologists. Current methods are typically modelling RRG as an image-to-text generation task that takes X-ray images as input and generates textual reports describing the corresponding clinical observations. However, the wording of the same clinical observation could have been influenced by the expression preference of radiologists. Nevertheless, such variability can be mitigated by normalizing textual reports into structured representations such as a graph structure. In this study, we attempt a novel paradigm for incorporating graph structural data into the RRG model. Our approach involves predicting graph labels based on visual features and subsequently initiating the decoding process through a template injection conditioned on the predicted labels. We trained and evaluated our model on the BioNLP 2024 Shared Task on Large-Scale Radiology Report Generation and submitted our results to the ViLMedic RRG leaderboard. Although our model showed a moderate ranking on the leaderboard, the results provide preliminary evidence for the feasibility of this new paradigm, warranting further exploration and refinement.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Publisher:	Association for Computational Linguistics
Date of First Compliant Deposit:	30 September 2024
Last Modified:	11 Oct 2024 11:05
URI:	https://orca.cardiff.ac.uk/id/eprint/172473

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