论文标题
寻找生活：从合成数据中学习以检测视频中的生命体征
In Search of Life: Learning from Synthetic Data to Detect Vital Signs in Videos
论文作者
论文摘要
在视频中自动检测到视频中的生命体征，例如对心脏和呼吸率的估计，是计算机视觉中具有挑战性的研究问题，在医学领域中具有重要的应用。解决这项任务的主要困难之一是缺乏足够的监督培训数据，这严重限制了强大的深神经网络的使用。在本文中，我们通过一种新颖的深度学习方法来解决这种局限性，在这种方法中，培训了一个经常性的深神经网络，以从纯合成数据中检测红外热域中的生命体征。最令人惊讶的是，我们的合成训练数据生成的新方法是一般的，相对简单，几乎没有先前的医疗领域知识。此外，我们的系统以纯粹的自动方式进行训练，不需要人类注释，还学会了预测每个时刻的呼吸或心脏强度信号，并检测到与给定任务最相关的感兴趣区域，例如呼吸的情况下的鼻子区域。我们测试了我们提出的系统在最近的LCA数据集中的有效性，并获得最先进的结果。
Automatically detecting vital signs in videos, such as the estimation of heart and respiration rates, is a challenging research problem in computer vision with important applications in the medical field. One of the key difficulties in tackling this task is the lack of sufficient supervised training data, which severely limits the use of powerful deep neural networks. In this paper we address this limitation through a novel deep learning approach, in which a recurrent deep neural network is trained to detect vital signs in the infrared thermal domain from purely synthetic data. What is most surprising is that our novel method for synthetic training data generation is general, relatively simple and uses almost no prior medical domain knowledge. Moreover, our system, which is trained in a purely automatic manner and needs no human annotation, also learns to predict the respiration or heart intensity signal for each moment in time and to detect the region of interest that is most relevant for the given task, e.g. the nose area in the case of respiration. We test the effectiveness of our proposed system on the recent LCAS dataset and obtain state-of-the-art results.
