  In the past decade, deep learning (DL) has achieved unprecedented success in
numerous fields including computer vision, natural language processing, and
healthcare. In particular, DL is experiencing an increasing development in
applications for advanced medical image analysis in terms of analysis,
segmentation, classification, and furthermore. On the one hand, tremendous
needs that leverage the power of DL for medical image analysis are arising from
the research community of a medical, clinical, and informatics background to
jointly share their expertise, knowledge, skills, and experience. On the other
hand, barriers between disciplines are on the road for them often hampering a
full and efficient collaboration. To this end, we propose our novel open-source
platform, i.e., MeDaS -- the MeDical open-source platform as Service. To the
best of our knowledge, MeDaS is the first open-source platform proving a
collaborative and interactive service for researchers from a medical background
easily using DL related toolkits, and at the same time for scientists or
engineers from information sciences to understand the medical knowledge side.
Based on a series of toolkits and utilities from the idea of RINV (Rapid
Implementation aNd Verification), our proposed MeDaS platform can implement
pre-processing, post-processing, augmentation, visualization, and other phases
needed in medical image analysis. Five tasks including the subjects of lung,
liver, brain, chest, and pathology, are validated and demonstrated to be
efficiently realisable by using MeDaS.
