Human-in-the-loop deep learning retinal image classification with customized loss function

Suhev Shakya; Mariana Vasquez; Yiyang Wang; Roselyn Tchoua; Jacob Furst; Daniela Raicu

doi:10.1117/12.2611173

4 April 2022 Human-in-the-loop deep learning retinal image classification with customized loss function

Suhev Shakya, Mariana Vasquez, Yiyang Wang, Roselyn Tchoua, Jacob Furst, Daniela Raicu

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120331Y (2022) https://doi.org/10.1117/12.2611173
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

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, i.e., the classifier provided a correct prediction label but looked at the wrong region. 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. The model learns both to classify the images and the explanations (ground truth) using this loss. Our results indicate that the proposed method improves the model explanation accuracy over the baseline model by 85% while maintaining a high classification accuracy of over 95%.

Citation Download Citation

Suhev Shakya, Mariana Vasquez, Yiyang Wang, Roselyn Tchoua, Jacob Furst, and Daniela Raicu "Human-in-the-loop deep learning retinal image classification with customized loss function", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120331Y (4 April 2022); https://doi.org/10.1117/12.2611173

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