Kayser, M;
Emde, C;
Camburu, OM;
Parsons, G;
Papiez, B;
Lukasiewicz, T;
(2022)
Explaining Chest X-Ray Pathologies in Natural Language.
In: Wang, L and Dou, Q and Fletcher, PT and Speidel, S and Li, S, (eds.)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022.
(pp. pp. 701-713).
Springer: Cham, Switzerland.
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Abstract
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medical imaging, have often been shown to convey limited information, be overly reassuring, or lack robustness. In this work, we introduce the task of generating natural language explanations (NLEs) to justify predictions made on medical images. NLEs are human-friendly and comprehensive, and enable the training of intrinsically explainable models. To this goal, we introduce MIMIC-NLE, the first, large-scale, medical imaging dataset with NLEs. It contains over 38,000 NLEs, which explain the presence of various thoracic pathologies and chest X-ray findings. We propose a general approach to solve the task and evaluate several architectures on this dataset, including via clinician assessment.
| Type: | Proceedings paper |
|---|---|
| Title: | Explaining Chest X-Ray Pathologies in Natural Language |
| Event: | International Conference on Medical Image Computing and Computer-Assisted Intervention |
| Location: | Singapore, SINGAPORE |
| Dates: | 18 Sep 2022 - 22 Sep 2022 |
| ISBN-13: | 9783031164422 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-16443-9_67 |
| Publisher version: | https://doi.org/10.1007/978-3-031-16443-9_67 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Life Sciences & Biomedicine, Computer Science, Interdisciplinary Applications, Imaging Science & Photographic Technology, Radiology, Nuclear Medicine & Medical Imaging, Computer Science, Chest X-rays, Natural language explanations, XAI, HEALTH |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10164339 |
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