Label-Efficient Multi-Task Segmentation using Contrastive Learning

Obtaining annotations for 3D medical images is expensive and time-consuming, despite its importance for automating segmentation tasks. Although multi-task learning is considered an effective method for training segmentation models using small amounts of annotated data, a systematic understanding of...

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Bibliographic Details
Published in:arXiv.org 2020-09
Main Authors: Iwasawa, Junichiro, Hirano, Yuichiro, Sugawara, Yohei
Format: Article
Language:English
Subjects:
Annotations
Image segmentation
Learning
Medical imaging
Regularization
Training
Online Access:Get full text
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Summary:Obtaining annotations for 3D medical images is expensive and time-consuming, despite its importance for automating segmentation tasks. Although multi-task learning is considered an effective method for training segmentation models using small amounts of annotated data, a systematic understanding of various subtasks is still lacking. In this study, we propose a multi-task segmentation model with a contrastive learning based subtask and compare its performance with other multi-task models, varying the number of labeled data for training. We further extend our model so that it can utilize unlabeled data through the regularization branch in a semi-supervised manner. We experimentally show that our proposed method outperforms other multi-task methods including the state-of-the-art fully supervised model when the amount of annotated data is limited.
ISSN:2331-8422