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The goal of our research is to create a comprehensive and flexible librarythat is easy to use for medical imaging research, and capable of handlinggrayscale images, multiple inputs (both images and tabular data), andmulti-label tasks. We have named it Nervus. Based on the PyTorch library, whichis suitable for AI for research purposes, we created a four-part model tohandle comprehensive inputs and outputs. Nervus consists of four parts. Firstis the dataloader, then the feature extractor, the feature mixer, and finallythe classifier. The dataloader preprocesses the input data, the featureextractor extracts the features between the training data and ground truthlabels, feature mixer mixes the features of the extractors, and the classifierclassifies the input data from feature mixer based on the task. We have createdNervus, which is a comprehensive and flexible model library that is easy to usefor medical imaging research which can handle grayscale images, multi-inputsand multi-label tasks. This will be helpful for researchers in the field ofradiology.

Nervus: A Comprehensive Deep Learning Classification, Regression, and Prognostication Tool for both Medical Image and Clinical Data Analysis