论文标题
具有一致图像报告生成的循环生成对抗网络，用于解释医学图像分析
Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis
论文作者
论文摘要
我们提出了一个可解释的医学图像标签和解释的新框架。医学图像需要专业的专业人员进行解释，并（通常）通过详细的文本报告（通常）解释。与以前关注来自图像或反之亦然的医疗报告生成的先前方法不同，我们在新颖地生成了一致的图像 - 报告使用环状生成对抗网络（Cyclegan）的报告对；因此，生成的报告将充分解释医学图像，而有效地（充分）（充分）有效表征文本的报告生成的图像类似于原始图像。这项工作的目的是通过将人用户指向类似的情况来支持诊断决定，从而为模型诊断胸部X射线图像的模型产生值得信赖和忠实的解释。除了启用透明的医学图像标签和解释外，我们还实现了与先前方法相当的报告和基于图像的标签，包括在某些情况下的最先进的性能，这是印第安纳州胸部X射线数据集的实验证明的
We present a novel framework for explainable labeling and interpretation of medical images. Medical images require specialized professionals for interpretation, and are explained (typically) via elaborate textual reports. Different from prior methods that focus on medical report generation from images or vice-versa, we novelly generate congruent image--report pairs employing a cyclic-Generative Adversarial Network (cycleGAN); thereby, the generated report will adequately explain a medical image, while a report-generated image that effectively characterizes the text visually should (sufficiently) resemble the original. The aim of the work is to generate trustworthy and faithful explanations for the outputs of a model diagnosing chest x-ray images by pointing a human user to similar cases in support of a diagnostic decision. Apart from enabling transparent medical image labeling and interpretation, we achieve report and image-based labeling comparable to prior methods, including state-of-the-art performance in some cases as evidenced by experiments on the Indiana Chest X-ray dataset
