MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysis

• URL: http://arxiv.org/abs/2308.16561v1
• Date: Thu, 31 Aug 2023 08:54:59 GMT
• Title: MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysis
• Authors: Trinh Thi Le Vuong and Jin Tae Kwak
• Abstract summary: A lack of quality data is a common issue when it comes to a specific task in computational pathology. We propose to exploit knowledge distillation, i.e., utilize the existing model to learn a new, target model. We employ a student-teacher framework to learn a target model from a pre-trained, teacher model without direct access to source data.
• Score: 5.396167537615578
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: There is no doubt that advanced artificial intelligence models and high quality data are the keys to success in developing computational pathology tools. Although the overall volume of pathology data keeps increasing, a lack of quality data is a common issue when it comes to a specific task due to several reasons including privacy and ethical issues with patient data. In this work, we propose to exploit knowledge distillation, i.e., utilize the existing model to learn a new, target model, to overcome such issues in computational pathology. Specifically, we employ a student-teacher framework to learn a target model from a pre-trained, teacher model without direct access to source data and distill relevant knowledge via momentum contrastive learning with multi-head attention mechanism, which provides consistent and context-aware feature representations. This enables the target model to assimilate informative representations of the teacher model while seamlessly adapting to the unique nuances of the target data. The proposed method is rigorously evaluated across different scenarios where the teacher model was trained on the same, relevant, and irrelevant classification tasks with the target model. Experimental results demonstrate the accuracy and robustness of our approach in transferring knowledge to different domains and tasks, outperforming other related methods. Moreover, the results provide a guideline on the learning strategy for different types of tasks and scenarios in computational pathology. Code is available at: \url{https://github.com/trinhvg/MoMA}.

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