Understanding Deep Neural Network Predictions for Medical Imaging Applications
Redha Ali, Supun Samudika De Silva, Nathan Kremer Kueterman
Computer-aided detection has been a research area attracting great interest in the past decade. Machine learning algorithms have been utilized extensively for this application as they provide a valuable second opinion to the doctors. Despite several machine learning models being available for medical imaging applications, not many have been implemented in the real-world due to the uninterpretable nature of the decisions made by the network. In this paper, we investigate the results provided by deep neural networks for the detection of malaria, diabetic retinopathy, brain tumor, and tuberculosis in different imaging modalities. We visualize the class activation mappings for all the applications in order to enhance the understanding of these networks. This type of visualization, along with the corresponding network performance metrics, would aid the data science experts in better understanding of their models as well as assisting doctors in their decision-making process.
Russell C. Hardie, Barath Narayanan
Primary Advisor's Department
Electrical and Computer Engineering
Stander Symposium Posters, School of Engineering
United Nations Sustainable Development Goals
Good Health and Well-Being
"Understanding Deep Neural Network Predictions for Medical Imaging Applications" (2020). Stander Symposium Projects. 1907.