Self-supervised learning forclassification of anatomic featureson kidney biopsy whole slide images

The technique of whole slide imaging (WSI) boosts the application of deeplearning in medical imaging analysis and computational pathology. How-ever, fully supervised learning stucks into bottlenecks due to the heavyreliance on manual annotations, which requires specific expertise and ex-pensive cost. Self-supervised learning would be a potential solution, whichis supervised by the signals generated from itself. It has been provedto perform as well as supervised learning on ImageNet in classificationtasks. Yet, its performance on medical image classification is unexplored.This study verifies the effectiveness of four self-supervised learning to de-tect anatomic structures on kidney biopsy WSI, including SimCLR, MoCo,SwAV and Barlow Twins. In the pretext-task, these self-supervised learn-ing algorithms are trained in 500 epochs with the same backbone archi-tecture, ResNet-50, which is initialized by the weights pre-trained on Im-ageNet correspondingly. The evaluation protocol is a...Show moreThe technique of whole slide imaging (WSI) boosts the application of deeplearning in medical imaging analysis and computational pathology. How-ever, fully supervised learning stucks into bottlenecks due to the heavyreliance on manual annotations, which requires specific expertise and ex-pensive cost. Self-supervised learning would be a potential solution, whichis supervised by the signals generated from itself. It has been provedto perform as well as supervised learning on ImageNet in classificationtasks. Yet, its performance on medical image classification is unexplored.This study verifies the effectiveness of four self-supervised learning to de-tect anatomic structures on kidney biopsy WSI, including SimCLR, MoCo,SwAV and Barlow Twins. In the pretext-task, these self-supervised learn-ing algorithms are trained in 500 epochs with the same backbone archi-tecture, ResNet-50, which is initialized by the weights pre-trained on Im-ageNet correspondingly. The evaluation protocol is a semi-supervisedlinear classifier, implemented by using multi-nomial logistic regression.The results of the classification task show the features extracted by thefour algorithms all achieve good accuracy scores, higher than 85% withonly 10% labels. Among them, SwAV outperforms the other algorithmsfrom the perspective of overview and each class. Through this study,self-supervised learning algorithms exhibit the potential for more complextasks related to renal pathology.Show less

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Self-supervised learning forclassification of anatomic featureson kidney biopsy whole slide images