Tech and Public Health: Bioimaging and Machine Learning at the Bedside | Online
About this event
Featured Expert:Dr. Quinn leads a research lab at UGA that focuses on cutting-edge computational methods for bioimaging in public health applications.
Overview: Addressing public health concerns requires large-scale collaboration, which is impossible without objective baseline assessments and openly-available analytics tools. This talk discusses promising initial results in the area of respiratory disease recognition.
What you'll take away: Workshop attendees will see how computer vision theory can be applied to answering open questions in public health within the context of the Python ecosystem.
Why it matters: Biomedical research and subsequent bedside applications have traditionally been siloed and proprietary endeavors. Open source tools and techniques will provide public health officials with powerful methods for addressing critical health concerns.
Takeaways:
- How bioimaging can be combined with open source analytic tools to develop quantitative assessment baselines for biomedical researchers and clinicians
- An overview of developing an end-to-end Python machine learning workflow to identify abnormalities in biomedical video data
- Our pipeline achieved an overall accuracy of 93% in distinguishing normal behavior in biomedical videos from abnormal behavior
