  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.
