Self-Supervised Pretraining for 2D Medical Image Segmentation
- URL: http://arxiv.org/abs/2209.00314v1
- Date: Thu, 1 Sep 2022 09:25:22 GMT
- Title: Self-Supervised Pretraining for 2D Medical Image Segmentation
- Authors: Andr\'as Kalapos and B\'alint Gyires-T\'oth
- Abstract summary: Self-supervised learning offers a way to lower the need for manually annotated data by pretraining models for a specific domain on unlabelled data.
We find that self-supervised pretraining on natural images and target-domain-specific images leads to the fastest and most stable downstream convergence.
In low-data scenarios, supervised ImageNet pretraining achieves the best accuracy, requiring less than 100 annotated samples to realise close to minimal error.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Supervised machine learning provides state-of-the-art solutions to a wide
range of computer vision problems. However, the need for copious labelled
training data limits the capabilities of these algorithms in scenarios where
such input is scarce or expensive. Self-supervised learning offers a way to
lower the need for manually annotated data by pretraining models for a specific
domain on unlabelled data. In this approach, labelled data are solely required
to fine-tune models for downstream tasks. Medical image segmentation is a field
where labelling data requires expert knowledge and collecting large labelled
datasets is challenging; therefore, self-supervised learning algorithms promise
substantial improvements in this field. Despite this, self-supervised learning
algorithms are used rarely to pretrain medical image segmentation networks. In
this paper, we elaborate and analyse the effectiveness of supervised and
self-supervised pretraining approaches on downstream medical image
segmentation, focusing on convergence and data efficiency. We find that
self-supervised pretraining on natural images and target-domain-specific images
leads to the fastest and most stable downstream convergence. In our experiments
on the ACDC cardiac segmentation dataset, this pretraining approach achieves
4-5 times faster fine-tuning convergence compared to an ImageNet pretrained
model. We also show that this approach requires less than five epochs of
pretraining on domain-specific data to achieve such improvement in the
downstream convergence time. Finally, we find that, in low-data scenarios,
supervised ImageNet pretraining achieves the best accuracy, requiring less than
100 annotated samples to realise close to minimal error.
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