Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-Rays
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by
Dewen Zeng, John N. Kheir, Peng Zeng, Yiyu Shi
2021
Abstract
Contrastive learning has been proved to be a promising technique for
image-level representation learning from unlabeled data. Many existing works
have demonstrated improved results by applying contrastive learning in
classification and object detection tasks for either natural images or medical
images. However, its application to medical image segmentation tasks has been
limited. In this work, we use lung segmentation in chest X-rays as a case study
and propose a contrastive learning framework with temporal correlated medical
images, named CL-TCI, to learn superior encoders for initializing the
segmentation network. We adapt CL-TCI from two state-of-the-art contrastive
learning methods-MoCo and SimCLR. Experiment results on three chest X-ray
datasets show that under two different segmentation backbones, U-Net and
Deeplab-V3, CL-TCI can outperform all baselines that do not incorporate any
temporal correlation in both semi-supervised learning setting and transfer
learning setting with limited annotation. This suggests that information among
temporal correlated medical images can indeed improve contrastive learning
performance. Between the two variations of CL-TCI, CL-TCI adapted from MoCo
outperforms CL-TCI adapted from SimCLR in most settings, indicating that more
contrastive samples can benefit the learning process and help the network learn
high-quality representations.
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