论文标题
从单访问到基于图像的多访问模型：单访问模型足以预测阻塞性肾结通
From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis
论文作者
论文摘要
先前的工作表明，深度学习使用肾脏超声图像预测肾脏阻塞的潜力。但是，这些基于图像的分类器已经接受了单访推断的目标。我们比较视频动作识别（即卷积池，LSTM，TSM）的方法，以适应单访问卷积模型以处理多次访问推理。我们证明，合并患者过去的医院就诊的图像仅为预测阻塞性肾脏病的预测提供了很小的好处。因此，纳入先前的超声是有益的，但是基于最新超声的预测对于患者风险分层就足够了。
Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.
