As the COVID-19 pandemic aggravated the excessive workload of doctors globally, the demand for computer aided methods in medical imaging analysis increased even further. Such tools can result in more robust diagnostic pipelines which are less prone to human errors. In our paper, we present a deep neural network to which we refer to as Attention BCDU-Net, and apply it to the task of lung and heart segmentation from chest X-ray (CXR) images, a basic but ardous step in the diagnostic pipeline, for instance for the detection of cardiomegaly. We show that the fine-tuned model exceeds previous state-of-the-art results, reaching $98.1\pm 0.1\%$ Dice score and $95.2\pm 0.1\%$ IoU score on the dataset of Japanese Society of Radiological Technology (JSRT). Besides that, we demonstrate the relative simplicity of the task by attaining surprisingly strong results with training sets of size 10 and 20: in terms of Dice score, $97.0\pm 0.8\%$ and $97.3\pm 0.5$, respectively, while in terms of IoU score, $92.2\pm 1.2\%$ and $93.3\pm 0.4\%$, respectively. To achieve these scores, we capitalize on the mixup augmentation technique, which yields a remarkable gain above $4\%$ IoU score in the size 10 setup.
翻译:由于COVID-19大流行加剧了全球医生的过度工作量,对医疗成像分析中计算机辅助方法的需求进一步增加,这些工具可以导致更强大的诊断管道,不易发生人类错误。我们在报告中提出了一个深神经网络,我们称之为注意BDDU-Net,并将其用于胸X射线(CXR)图像中的肺和心脏分解任务,这是诊断管道中一个基本但非常大的步骤,例如心脏检查。我们显示,微调模型超过了以往的最新结果,达到98.1美元0.1% 的诊断管道,在日本放射技术学会(JSRT)的数据集中达到9.52美元0.1 美元 美元 的诊断管道。 此外,我们通过10和20级的成套培训取得了令人惊讶的强劲成果,显示了这项任务的相对简单性:就Dice分数而言,97.00 0.8美元和97.3美元 0.5美元,而就IOU分而言,我们比IOU分分得分达到98.1美元,在0.1美元中达到创0.1美元分。 此外,我们用0.4 1.2m 和创得分,我们分别达到创0.4美元得分。