# Self-Supervised Learning for Segmentation

arXiv: https://arxiv.org/abs/2101.05456 · HTML: https://mlsanity.com/paper/2101.05456/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 15 references that are in the map.
- First posted on arXiv 14 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Multi-task Self-Supervised Visual Learning](https://mlsanity.com/paper/1708.07860/) (2017)
- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)
- [Tracking Emerges by Colorizing Videos](https://mlsanity.com/paper/1806.09594/) (2018)
- [Learning to See by Moving](https://mlsanity.com/paper/1505.01596/) (2015)
- [Self-Supervised Learning for Spinal MRIs](https://mlsanity.com/paper/1708.00367/) (2017)
- [Non-Rigid Image Registration Using Self-Supervised Fully Convolutional Networks without Training Data](https://mlsanity.com/paper/1801.04012/) (2018)
- [An attempt at beating the 3D U-Net](https://mlsanity.com/paper/1908.02182/) (2019)
- [Knowledge Transfer for Melanoma Screening with Deep Learning](https://mlsanity.com/paper/1703.07479/) (2017)

## Built on by (most cited first, 4 of 4)

- [BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net models](https://mlsanity.com/paper/2112.03916/) (2021)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [Graph Neural Networks: a bibliometrics overview](https://mlsanity.com/paper/2201.01188/) (2022)
- [Points2Polygons: Context-Based Segmentation from Weak Labels Using Adversarial Networks](https://mlsanity.com/paper/2106.02804/) (2021)

## Abstract (arXiv)

Self-supervised learning is emerging as an effective substitute for transfer learning from large datasets. In this work, we use kidney segmentation to explore this idea. The anatomical asymmetry of kidneys is leveraged to define an effective proxy task for kidney segmentation via self-supervised learning. A siamese convolutional neural network (CNN) is used to classify a given pair of kidney sections from CT volumes as being kidneys of the same or different sides. This knowledge is then transferred for the segmentation of kidneys using another deep CNN using one branch of the siamese CNN as the encoder for the segmentation network. Evaluation results on a publicly available dataset containing computed tomography (CT) scans of the abdominal region shows that a boost in performance and fast convergence can be had relative to a network trained conventionally from scratch. This is notable given that no additional data/expensive annotations or augmentation were used in training.