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My goal is to democratize access to healthcare to provide the highest quality healthcare to everyone (specifically those not served by the current system; 8% in the US and 25% globally). Automation and AI can increase the supply of providers to fill this need. I am working to identify and overcome issues limiting the deployment of AI tools in healthcare. My core research directions are representation learning, generalization, and model interpretability.

1 sponsor has funded ieee8023’s work.

@taskswithcode

Featured work

  1. mlmed/torchxrayvision

    TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

    Jupyter Notebook 951
  2. ieee8023/blindtool

    BlindTool – A mobile app that gives a "sense of vision" to the blind with deep learning

    Java 12
  3. ieee8023/covid-chestxray-dataset

    We are building an open database of COVID-19 cases with chest X-ray or CT images.

    Jupyter Notebook 3,009
  4. ieee8023/NeuralNetwork-Examples

    The same small networks implemented in different frameworks

    Jupyter Notebook 70
  5. mlmed/chester-xray

    Chester the AI Radiology Assistant

    JavaScript 161
  6. ieee8023/countception

    Count-Ception: Counting by Fully Convolutional Redundant Counting

    Jupyter Notebook 52

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