Human-in-the-loop deep learning retinal image classification with customized loss function
In person: 23 February 2022 • 5:30 PM - 7:00 PM PST
Age-related Macular Degeneration (AMD) is a significant health burden that can lead to irreversible vision loss in the elderly population. Accurately classifying Optical Coherence Tomography (OCT) images is vital in computer-aided diagnosis (CAD) of AMD. Most CAD studies focus on improving classification results but ignore the fact that a classifier may predict a correct image label for the wrong reasons. We propose a human-in-the-loop OCT image classification scheme that allows users to provide feedback on model explanation during the training process to address this limitation. We innovatively integrate a custom loss function with our expert’s annotation of the OCT images along with the model’s explanation. Our results indicate that the proposed method improves the model explanation correction over the baseline model by 85% while maintaining a high classification accuracy of over 95%.
Columbus State Univ. (United States)