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.
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