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Label-Efficient Multi-Task Segmentation using Contrastive Learning
[article]
2020
arXiv
pre-print
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,
arXiv:2009.11160v1
fatcat:end42iytbjcslmlweu3bxua2ce