Controllable Chest X-Ray Report Generation from Longitudinal Representations
Francesco Dalla Serra, Chaoyang Wang, Fani Deligianni, Jeff Dalton, Alison O’Neil
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.- Anthology ID:
- 2023.findings-emnlp.325
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4891–4904
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.325
- DOI:
- 10.18653/v1/2023.findings-emnlp.325
- Cite (ACL):
- Francesco Dalla Serra, Chaoyang Wang, Fani Deligianni, Jeff Dalton, and Alison O’Neil. 2023. Controllable Chest X-Ray Report Generation from Longitudinal Representations. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4891–4904, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Controllable Chest X-Ray Report Generation from Longitudinal Representations (Dalla Serra et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2023.findings-emnlp.325.pdf