Controllable Chest X-Ray Report Generation from Longitudinal Representations

Dalla Serra, F., Wang, C., Deligianni, F. , Dalton, J. and O'Neil, A. Q. (2023) Controllable Chest X-Ray Report Generation from Longitudinal Representations. In: The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), Singapore, 06-10 Dec 2023, pp. 4891-4904. (doi: 10.18653/v1/2023.findings-emnlp.325)

Abstract

Radiology reports are detailed text descriptions of the content of medical scans. Each report describes the presence/absence and location of relevant clinical findings, commonly including comparison with prior exams of the same patient to describe how they evolved. Radiology reporting is a time-consuming process, and scan results are often subject to delays. One strategy to speed up reporting is to integrate automated reporting systems, however clinical deployment requires high accuracy and interpretability. Previous approaches to automated radiology reporting generally do not provide the prior study as input, precluding comparison which is required for clinical accuracy in some types of scans, and offer only unreliable methods of interpretability. Therefore, leveraging an existing visual input format of anatomical tokens, we introduce two novel aspects: (1) longitudinal representation learning – we input the prior scan as an additional input, proposing a method to align, concatenate and fuse the current and prior visual information into a joint longitudinal representation which can be provided to the multimodal report generation model; (2) sentence-anatomy dropout – a training strategy for controllability in which the report generator model is trained to predict only sentences from the original report which correspond to the subset of anatomical regions given as input. We show through in-depth experiments on the MIMIC-CXR dataset how the proposed approach achieves state-of-the-art results while enabling anatomy-wise controllable report generation.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Deligianni, Dr Fani and Dalton, Dr Jeff and Dalla Serra, Francesco
• Authors: Dalla Serra, F., Wang, C., Deligianni, F., Dalton, J., and O'Neil, A. Q.
• College/School: College of Science and Engineering > School of Computing Science
• Copyright Holders: Copyright © 2023 Association for Computational Linguistics
• First Published: First published in Findings of the Association for Computational Linguistics: EMNLP 2023
• Publisher Policy: Reproduced under a Creative Commons license

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