Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysis

• URL: http://arxiv.org/abs/2206.00344v1
• Date: Wed, 1 Jun 2022 09:20:30 GMT
• Title: Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysis
• Authors: Marin Ben\v{c}evi\'c, Marija Habijan, Irena Gali\'c, Aleksandra Pizurica
• Abstract summary: We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies. We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60% of the labeled data.
• Score: 64.4093648042484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the largest problems in medical image processing is the lack of annotated data. Labeling medical images often requires highly trained experts and can be a time-consuming process. In this paper, we evaluate a method of reducing the need for labeled data in medical image object detection by using self-supervised neural network pretraining. We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies. The networks are pretrained on a percentage of the dataset without labels and then fine-tuned on the rest of the dataset. We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60\% of the labeled data. We also show that it is possible to increase the maximum performance of a fully-supervised model by adding a self-supervised pretraining step, and this effect can be observed with even a small amount of unlabeled data for pretraining.

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