# Label-Efficient Multi-Task Segmentation using Contrastive Learning

arXiv: https://arxiv.org/abs/2009.11160 · HTML: https://mlsanity.com/paper/2009.11160/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2111.10989/) (2021)
- [Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2105.12924/) (2021)
- [Contrastive and Selective Hidden Embeddings for Medical Image Segmentation](https://mlsanity.com/paper/2201.08779/) (2022)

## Abstract (arXiv)

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.