MEDAS: an open-source platform as a service to help break the walls between medicine and informatics

Liang Zhang, Johann Li, Ping Li, Xiaoyuan Lu, Maoguo Gong, Peiyi Shen, Guangming Zhu, Syed Afaq Shah, Mohammed Bennamoun, Kun Qian, Björn W. Schuller

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields, such as computer vision and healthcare. Particularly, DL is experiencing an increasing development in advanced medical image analysis applications in terms of segmentation, classification, detection, and other tasks. On the one hand, tremendous needs that leverage DL’s power for medical image analysis arise from the research community of a medical, clinical, and informatics background to share their knowledge, skills, and experience jointly. 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 providing collaborative and interactive services for researchers from a medical background using DL-related toolkits easily and for scientists or engineers from informatics modeling faster. Based on tools and utilities from the idea of RINV (Rapid Implementation aNd Verification), our proposed platform implements tools in pre-processing, post-processing, augmentation, visualization, and other phases needed in medical image analysis. Five tasks, concerning lung, liver, brain, chest, and pathology, are validated and demonstrated to be efficiently realizable by using MEDAS. MEDAS is available at

Original languageEnglish
Pages (from-to)6547-6567
Number of pages21
JournalNeural Computing and Applications
Issue number8
Publication statusPublished - Apr 2022


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