This study aims to investigate the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Although current classifiers achieve high performance using chest X-ray images alone, our interviews with radiologists indicate that clinical data is highly informative and essential for interpreting images and making proper diagnoses. In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data). Since these data modalities are in different dimensional spaces, we propose a spatial arrangement strategy, termed spatialization, to facilitate the multimodal learning process in a Mask R-CNN model. We performed an extensive experimental evaluation comprising three datasets with different modalities: MIMIC CXR (chest X-ray images), MIMIC IV-ED (patients' clinical data), and REFLACX (annotations of disease locations in chest X-rays). Results show that incorporating patients' clinical data in a DL model together with the proposed fusion methods improves the performance of disease localization in chest X-rays by 12\% in terms of Average Precision compared to a standard Mask R-CNN using only chest X-rays. Further ablation studies also emphasize the importance of multimodal DL architectures and the incorporation of patients' clinical data in disease localisation. The architecture proposed in this work is publicly available to promote the scientific reproducibility of our study (https://github.com/ChihchengHsieh/multimodal-abnormalities-detection).

翻译：本研究旨在探究患者临床信息对深度学习分类器在胸部X光图像病灶定位性能中的影响。尽管现有分类器仅依赖胸部X光图像即能取得较高性能，但通过与放射科医生的访谈发现，临床数据对图像解读和正确诊断具有关键信息价值。本文提出一种包含两种融合方法的新型架构，使模型能够同时处理患者的临床数据（结构化数据）与胸部X光图像（图像数据）。针对这两类数据模态存在于不同维度空间的问题，我们提出一项名为"空间化"的空间排列策略，以促进Mask R-CNN模型中的多模态学习过程。我们进行了包含三个不同模态数据集的广泛实验评估：MIMIC CXR（胸部X光图像）、MIMIC IV-ED（患者临床数据）与REFLACX（胸部X光病灶位置标注）。结果表明，在深度学习模型中整合患者临床数据并采用所提出的融合方法，相较于仅使用胸部X光的标准Mask R-CNN模型，病灶定位的平均精度提升了12%。进一步的消融实验也强调了多模态深度学习架构及患者临床数据整合在病灶定位中的重要性。本文提出的架构已公开，以促进本研究的科学可复现性（https://github.com/ChihchengHsieh/multimodal-abnormalities-detection）。