Abstract
A radiology report typically comprises multiple sentences covering different aspects of an imaging examination. With some preprocessing effort, these sentences can be regrouped according to a predefined set of topics, allowing us to implement a straightforward two-stage model for chest X-ray radiology report generation. Firstly, a topic classifier detects relevant findings or abnormalities in an image. Secondly, a conditional report generator outputs sentences from an image conditioned on a given topic. We present experimental results on the test split of the MIMIC-CXR dataset for each stage separately and the system as a whole. Most notably, the proposed model outperforms previous works on several medical correctness metrics based on the CheXpert labeler, establishing a new state-of-the-art. The source code is available at https://github.com/PabloMessina/MedVQA/.
José Cañete
Expert Machine Learning Engineer | MSc. in Computer Science
My research interests include Artificial Intelligence and how to handle and optimize these systems for production environments.