Data science and integrative biomedical research to advance healthcare

Biomedical Informatics (BMI) is a multidisciplinary field that is motivated by our desire to improve diagnosis, clinical care, and human health, through novel computational approaches to use (and learn from) biomedical and clinical data. We use our expertise in computer science and informatics by developing various enabling tools, technologies, and algorithms to solve specific biomedical and clinical applications. And in doing so help advance our understanding of disease and treatment, and also develop useful software and applications. Members of the department work in a variety of areas that range from machine learning, healthcare middleware that leverages cloud computing, clinical information systems, clinically oriented image analysis, and biomedical knowledge modeling. The driving applications for the various ongoing projects include cancer research, organ transplant, HIV, medical imaging, radiation therapy, and clinical data analytics. All development work that is undertaken is free and open-source. We have had a diverse set of successful GSoC projects in the past. In previous years, GSoC students have worked on diverse projects such as: geospatial systems for exploring microscopy environments that leveraged Hadoop; GPU accelerated pipelines for computational analysis of digitized biopsies; interactive visualization platforms for viewing massive images (>1GB); systems for data agnostic sharing of biomedical research datasets; Apache Drill based data integration and de-duplication platform for SQL and NoSQL databases; CNN based high throughput analysis of digitized biopsies; A GUI for Tensorflow; integrated architectures for biomedical data integration and federation; and information visualization of heterogeneous medical data. Many of these projects have been published in reputable journals and presented at major conferences. Some of the projects proved to be so successful that they were adopted in major national/international biomedical research initiatives.

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  • python
  • java
  • deeplearning
  • medical imaging
  • tensorflow


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Department of Biomedical Informatics (BMI), Emory University School of Medicine 2021 Projects

  • Shubham Awasthi
    A Frontend For Loopsim Framework To Model Workflows With Closed Loops
    LoopSim framework aims to facilitate workflows with loops i.e workflows without specific start and end services. LoopSim requires an interface to...
  • Nishchal Singi
    A frontend for Niffler DICOM framework for machine learning pipelines and processing workflows
    This project aims to develop a potential frontend for Niffler. Niffler is currently a command-line tool where the user has to download it in their...
  • Chinmay Vibhute
    A Testing Framework for Niffler DICOM frameworks for Continuous Integration
    This Project aims to develop unit and integration tests for the Niffler modules and implement automated testing using Continuous Integration. The...
  • Aryan Verma
    CovCT: Development of an AI-based Android Application for distinguishing COVID-19 and Pneumonia using Computed Tomography Images and generate reports for analysis
    Development of an AI-based Android Application for distinguishing COVID-19 and Pneumonia using Computed Tomography Images.The project aims at...
  • Özgür Kara
    Graphical User Interface for OpenAI Gym
    The project is aiming to develop a GUI for the simulations of reinforcement learning so that users, particularly, the researchers can be able to test...
  • Viraj Patel
    Tensorflow GUI
    Tensorflow GUI will help everyone from beginners to professionals in their day to day work. People who are new to the field of deep learning can use...