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.

中文翻译：

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基于最少训练集的胸部X线肺和心脏分割

由于COVID-19大流行加剧了全球医生过多的工作量，因此医学影像分析对计算机辅助方法的需求进一步增加。这样的工具可以导致更健壮的诊断流水线，不易发生人为错误。在我们的论文中，我们提出了一个深层神经网络，我们将其称为Attention BCDU-Net，并将其应用于根据胸部X射线（CXR）图像进行肺和心脏分割的任务，这是诊断中的基本但艰巨的步骤管道，例如用于检测心脏肥大。我们显示，经过微调的模型超过了以前的最新结果，在日本放射技术学会（JSRT）的数据集上达到了$ 98.1 \ pm 0.1 \％$骰子得分和$ 95.2 \ pm 0.1 \％$ IoU得分）。除此之外，我们通过使用大小为10和20的训练集获得令人惊讶的强劲结果来证明任务的相对简单性：在Dice得分方面，分别为$ 97.0 \ pm 0.8 \％$和$ 97.3 \ pm 0.5 $，而在IoU得分方面，$ 92.2 \ pm 1.2 \％$和$ 93.3 \ pm 0.4 \％$。为了获得这些分数，我们利用混合增强技术，在10号尺寸设置中，IoU分数超过$ 4 \％$时，会产生显着的收益。