论文标题
达尔文：放射学研究成像研究的高度灵活平台
DARWIN: A Highly Flexible Platform for Imaging Research in Radiology
论文作者
论文摘要
为了进行放射组学或深度学习研究实验，放射科医生或医生需要掌握所需的编程技能，但是，当他们拥有有限的编码经验时，它们可能会令人沮丧且昂贵。在本文中，我们提出了一个灵活的研究平台Darwin，该平台具有用于医学成像研究的图形用户界面。我们的平台由一个放射组模块和一个深度学习模块组成。放射线模块可以提取1000多个维度功能（第一，第二和高阶），并提供许多可拖动的监督和无监督的机器学习模型。我们的深度学习模块整合了分类，检测和细分任务的艺术体系结构的状态。它允许用户手动选择超参数，或选择算法以自动搜索最佳算法。达尔文还为用户提供了为实验定义自定义管道的可能性。这些灵活性使放射学家能够轻松进行各种实验。
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.
