- About Us
- Services
- Stories
- Faisal Beg – Algorithms to Advance Research in Medicine
- Yasutaka Furukawa – Smart Building Technologies to Enhance Living Spaces and Create Opportunities
- Mo Chen – AI to Create Safe and Practical Robotics
- Sheelagh Carpendale – Understanding Data Through Interaction and Visualization
- Innovation to Improve 3D Navigation
- Voice AI is Helping Shoppers Make Better Decisions
- Geographic Information Science Can Help Better Track COVID-19
- Deep Learning to Inform Medical Diagnoses
- Protecting Killer Whales from Marine Traffic
- Using Big Data to Boost Athletic Performance
- Machine Reading for Literary Texts
- Finding a Cure for HIV with Big Data
- Linked Data for Women's History
- How Big Data Can Combat Fake News
- Algorithms for Safer Streets
- Discovering Wilde Data
- Deep Blue Data
- Big Data Meets Big Impact
- Previous Next Big Question Fund Projects
- Data Fellowships
- Using Data
- Upcoming Events
Interpretable Deep Learning for Medical Imaging
Interpretable Deep Learning for Medical Imaging related Clinical Decision Support
Project Team: Diane Gromala (Interactive Arts and Technology, SFU), Ghassan Hamarneh (Computing Science, SFU), Sheelagh Carpendale (Computing Science, SFU), Weina Jin (Interactive Arts and Technology, SFU).
Although Big Data and Deep Learning (DL) in medicine are advancing rapidly, their implementation into patient-care settings has not yet become widespread. One pivotal impediment is the black-box nature of DL models. Since physicians typically do not have an explanation for the model’s output, they are undertaking high risks if the models are predicting erroneously.
This project will develop and evaluate an interpretable DL system on medical imaging data that is explainable to doctors for their clinical decision support. We combine DL with visualization and interaction techniques to make it more interpretable. We then deploy the system at our partner hospital for identifying neuromuscular disorders. We will evaluate the resulting system with both DL practitioners and physicians. Both large, publicly available medical imaging datasets and the private MRI dataset from our clinical collaborators will be utilized. This project is interdisciplinary research of big data, information visualization, human-computer interaction and medicine.