Reading and interpreting chest X-ray images is one of the most radiologist's routines. However, it still can be challenging, even for the most experienced ones. Therefore, we proposed a multi-model deep learning-based automated chest X-ray report generator system designed to assist radiologists in their work. The basic idea of the proposed system is by utilizing multi binary-classification models for detecting multi abnormalities, with each model responsible for detecting one abnormality, in a single image. In this study, we limited the radiology abnormalities detection to only cardiomegaly, lung effusion, and consolidation. The system generates a radiology report by performing the following three steps: image pre-processing, utilizing deep learning models to detect abnormalities, and producing a report. The aim of the image pre-processing step is to standardize the input by scaling it to 128x128 pixels and slicing it into three segments, which covers the upper, lower, and middle parts of the lung. After pre-processing, each corresponding model classifies the image, resulting in a 0 (zero) for no abnormality detected and a 1 (one) for the presence of an abnormality. The prediction outputs of each model are then concatenated to form a 'result code'. The 'result code' is used to construct a report by selecting the appropriate pre-determined sentence for each detected abnormality in the report generation step. The proposed system is expected to reduce the workload of radiologists and increase the accuracy of chest X-ray diagnosis.

翻译：阅读和解读胸部X光图像是放射科医师最常见的日常工作之一，然而即便是经验最丰富的医师也可能面临挑战。为此，我们提出了一种基于多模型深度学习的自动化胸部X光报告生成系统，旨在辅助放射科医师完成工作。该系统的基本思路是通过多二元分类模型检测多种异常，每个模型负责检测单张图像中的一种异常。在本研究中，我们将放射学异常检测范围限定为心脏肥大、肺积液和肺实变。系统通过以下三个步骤生成放射学报告：图像预处理、利用深度学习模型检测异常及生成报告。图像预处理步骤的目标是将输入标准化至128×128像素，并将其分割为覆盖肺上部、中部和下部的三个片段。预处理后，每个对应模型对图像进行分类，输出0（零）表示未检测到异常，1（一）表示存在异常。各模型的预测结果随后拼接形成“结果代码”。在报告生成步骤中，利用该“结果代码”为每个检测到的异常选择预设语句，从而构建报告。本系统有望减轻放射科医师的工作负担，并提高胸部X光诊断的准确性。