Label-Efficient Multi-Task Segmentation using Contrastive Learning
- URL: http://arxiv.org/abs/2009.11160v1
- Date: Wed, 23 Sep 2020 14:12:17 GMT
- Title: Label-Efficient Multi-Task Segmentation using Contrastive Learning
- Authors: Junichiro Iwasawa, Yuichiro Hirano and Yohei Sugawara
- Abstract summary: We propose a multi-task segmentation model with a contrastive learning based subtask and compare its performance with other multi-task models.
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.
- Score: 0.966840768820136
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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