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

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Abstract

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 http://medas.bnc.org.cn/.

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Notes

  1. IaaS is the abbreviation of “Infrastructure as a Service”; PaaS is the abbreviation of “Platform as a Service”; SaaS is the abbreviation of “Software as a Service”; and CaaS is the abbreviation of “Container as a Service”.

  2. The container includes 6 cores of Intel® Xeon® Gold 5120 CPU, an NVIDIA Tesla V100(32G PCIe version), and 48 Gigabytes of memory.

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Acknowledgments

The authors would like to acknowledge all of the contributors to MEDAS: An open-source platform as a service to help break the walls between medicine and informatics. This work was supported by.—Shanghai Science and Technology Committee (No. 18411952100, No. 17411953500)

— National Natural Science Foundation of China (No. 62072358)

— National Key R&D Program of China under Grant (No. 2020YFF0304900, No. 2019YFB1311600).

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Author notes

  1. Liang Zhang and Johann Li have contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. Xidian University, Xi’an, China

    Liang Zhang, Johann Li, Maoguo Gong, Peiyi Shen & Guangming Zhu

  2. Data and Virtual Research Room, Shanghai Broadband Network Center, Shanghai, China

    Ping Li & Xiaoyuan Lu

  3. College of Science, Health, Engineering and Education, Murdoch University, Perth, Australia

    Syed Afaq Shah

  4. School of Computer Science and Software Engineering, The University of Western Australia, Crawley, Australia

    Mohammed Bennamoun

  5. School of Medical Technology, Beijing Institute of Technology, Beijing, China

    Kun Qian

  6. GLAM - Group on Language, Audio & Music, Imperial College London, London, UK

    Björn W. Schuller

  7. Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany

    Björn W. Schuller

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Zhang, L., Li, J., Li, P. et al. MEDAS: an open-source platform as a service to help break the walls between medicine and informatics. Neural Comput & Applic 34, 6547–6567 (2022). https://doi.org/10.1007/s00521-021-06750-9

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