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arXiv · 2009.00908

DARWIN: A Highly Flexible Platform for Imaging Research in Radiology

Lufan Chang·Wenjing Zhuang·Richeng Wu·Sai Feng·Hao Liu·Jing Yu·Jia Ding·Ziteng Wang·Jiaqi Zhang
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Abstract

To conduct a radiomics or deep learning research experiment, the radiologists or physicians need to grasp the needed programming skills, which, however, could be frustrating and costly when they have limited coding experience. In this paper, we present DARWIN, a flexible research platform with a graphical user interface for medical imaging research. Our platform is consists of a radiomics module and a deep learning module. The radiomics module can extract more than 1000 dimension features(first-, second-, and higher-order) and provided many draggable supervised and unsupervised machine learning models. Our deep learning module integrates state of the art architectures of classification, detection, and segmentation tasks. It allows users to manually select hyperparameters, or choose an algorithm to automatically search for the best ones. DARWIN also offers the possibility for users to define a custom pipeline for their experiment. These flexibilities enable radiologists to carry out various experiments easily.

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Lufan Chang, Wenjing Zhuang, Richeng Wu, Sai Feng, Hao Liu, Jing Yu, Jia Ding, Ziteng Wang, Jiaqi Zhang. 2020-09-02. DARWIN: A Highly Flexible Platform for Imaging Research in Radiology. https://arxiv.org/abs/2009.00908

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