Buscar

Clinically Correct Report Generation from Chest X-Rays Using Templates

RL1, Publisher: Link>


AUTHORS

Pablo Pino, Denis Parra, Cecilia Besa, Claudio Lagos


ABSTRACT

We address the task of automatically generating a medical report from chest X-rays. Many authors have proposed deep learning models to solve this task, but they focus mainly on improving NLP metrics, such as BLEU and CIDEr, which are not suitable to measure clinical correctness in clinical reports. In this work, we propose CNN-TRG, a Template-based Report Generation model that detects a set of abnormalities and verbalizes them via fixed sentences, which is much simpler than other state-of-the-art NLG methods and achieves better results in medical correctness metrics. We benchmark our model in the IU X-ray and MIMIC-CXR datasets against naive baselines as well as deep learning-based models, by employing the Chexpert labeler and MIRQI as clinical correctness evaluations, and NLP metrics as secondary evaluation. We also provide further evidence indicating that traditional NLP metrics are not suitable for this task by presenting their lack of robustness in multiple cases. We show that slightly altering a template-based model can increase NLP metrics considerably while maintaining high clinical performance. Our work contributes by a simple but effective approach for chest X-ray report generation, as well as by supporting a model evaluation focused primarily on clinical correctness metrics and secondarily on NLP metrics.

3 visualizaciones

Entradas Recientes

Ver todo

RL2, Publisher: Journal of Machine Learning Research, Link> AUTHORS Jorge Pérez, Pablo Barceló, Javier Marinkovic ABSTRACT Alternatives to recurrent neural networks, in particular, architectures bas

RL2, Publisher: https://github.com/pdm-book/community Link> AUTHORS Marcelo Arenas, Pablo Barceló, Leonid Libkin, Wim Martens, Andreas Pieris ABSTRACT This is a release of parts 1, 2, and 4 of the