Abstract
Self-supervised learning methods have been receiving wide attentions in recent years, where contrastive learning starts to show encouraging performance in many tasks in the field of computer vision. Contrastive learning methods build pre-training weight parameters by crafting positive/negative samples and optimizing their distance in the feature space. It is easy to construct positive/negative samples on natural images, but the methods cannot directly apply to histopathological images because of the unique characteristics of the images such as staining invariance and vertical flip invariance. This paper proposes a general method for constructing clinical-equivalent positive sample pairs on histopathological images for applying contrastive learning on histopathological images. Results on the PatchCamelyon benchmark show that our method can improve model accuracy up to 6% while reducing the training costs, as well as reducing reliance on labeled data.
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This research was supported in part by the Foundation of Shenzhen Science and Technology Innovation Committee (JCYJ20180507181527806).
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Qin, W., Jiang, S., Luo, L. (2022). Pathological Image Contrastive Self-supervised Learning. In: Xu, X., Li, X., Mahapatra, D., Cheng, L., Petitjean, C., Fu, H. (eds) Resource-Efficient Medical Image Analysis. REMIA 2022. Lecture Notes in Computer Science, vol 13543. Springer, Cham. https://doi.org/10.1007/978-3-031-16876-5_9
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