Increased Robustness in Chest X-Ray Classification Through Clinical Report-Driven Regularization

Abstract

In highly regulated areas such as healthcare there is a demand for explainable and trustworthy systems that are capable of providing some sort of foundation or logical reasoning to their functionality. Therefore, deep learning applications associated with such industry are increasingly required by this sense of accountability regarding their production value. Additionally, it is of utter importance to take advantage of all possible data resources, in order to achieve a greater amount of efficiency respecting such intelligent frameworks, while maintaining a realistic medical scenario. As a way to explore this issue, we propose two models trained with information retained in chest radiographs and regularized by the associated medical reports. We argue that the knowledge extracted from the free-radiology text, in a multimodal training context, promotes more coherence, leading to better decisions and interpretability saliency maps. Our proposed approach demonstrated to be more robust than their baseline counterparts, showing better classification performances, and also ensuring more concise, consistent and less dispersed saliency maps. Our proof-of-concept experiments were done using the publicly available multimodal radiology dataset MIMIC-CXR that contains a myriad of chest X-rays and its correspondent free-text reports.