Quintet margin loss for an improved knowledge distillation in histopathology image analysis

Trinh T. L. Vuong; Jin Tae Kwak

doi:10.1117/12.2654275

6 April 2023 Quintet margin loss for an improved knowledge distillation in histopathology image analysis

Trinh T. L. Vuong, Jin Tae Kwak

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124710J (2023) https://doi.org/10.1117/12.2654275
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Digital and computational pathology tools often suffer from a lack of relevant data. Although more and more data centers publicize the datasets, high-quality ground truth annotations may not be available in a timely manner. Herein, we propose a knowledge distillation framework that can utilize a teacher network that is already trained on a relatively larger amount of data and achieve accurate and robust performance on histopathology images by a student network. For an effective and efficient knowledge distillation, we introduce a quintet margin loss that pushes the student network not only to mimic the knowledge representation of the teacher network but also to outperform the teacher network on a target domain. We systematically evaluated the proposed approach. The results show that the proposed approach outperforms other competing models with and without different types of knowledge distillation methods.

Conference Presentation

Citation Download Citation

Trinh T. L. Vuong and Jin Tae Kwak "Quintet margin loss for an improved knowledge distillation in histopathology image analysis", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124710J (6 April 2023); https://doi.org/10.1117/12.2654275

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