论文标题
神经：用于医学图像和临床数据分析的全面深度学习分类，回归和预后工具
Nervus: A Comprehensive Deep Learning Classification, Regression, and Prognostication Tool for both Medical Image and Clinical Data Analysis
论文作者
论文摘要
我们研究的目的是创建一个易于用于医学成像研究的综合且灵活的库，并能够处理灰度图像，多个输入（图像和表格数据）和多标签任务。我们将其命名为Neverus。基于适合AI的Pytorch库，我们创建了一个四部分模型来处理全面的输入和输出。神经由四个部分组成。首先是数据加载程序，然后是功能提取器，功能混合器，最后是分类器。数据加载程序预处理输入数据，功能提取器提取训练数据和地面真实标签之间的功能，特征混合器混合了提取器的功能，分类器根据任务将输入数据分类。我们创建了Nervus，这是一个综合而灵活的模型库，易于用于医学成像研究，可以处理灰度图像，多输入和多标签任务。这将对放射学领域的研究人员有所帮助。
The goal of our research is to create a comprehensive and flexible library that is easy to use for medical imaging research, and capable of handling grayscale images, multiple inputs (both images and tabular data), and multi-label tasks. We have named it Nervus. Based on the PyTorch library, which is suitable for AI for research purposes, we created a four-part model to handle comprehensive inputs and outputs. Nervus consists of four parts. First is the dataloader, then the feature extractor, the feature mixer, and finally the classifier. The dataloader preprocesses the input data, the feature extractor extracts the features between the training data and ground truth labels, feature mixer mixes the features of the extractors, and the classifier classifies the input data from feature mixer based on the task. We have created Nervus, which is a comprehensive and flexible model library that is easy to use for medical imaging research which can handle grayscale images, multi-inputs and multi-label tasks. This will be helpful for researchers in the field of radiology.
