# Pixel-global Self-supervised Learning with Uncertainty-aware Context Stabilizer

arXiv: https://arxiv.org/abs/2210.00646 · HTML: https://mlsanity.com/paper/2210.00646/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 43 references that are in the map.
- First posted on arXiv 2 Oct 2022.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2011.09157/) (2020)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/2011.10043/) (2020)
- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)
- [Semi-supervised Left Atrium Segmentation with Mutual Consistency Training](https://mlsanity.com/paper/2103.02911/) (2021)
- [BYOL works even without batch statistics](https://mlsanity.com/paper/2010.10241/) (2020)
- [Self-supervised learning methods and applications in medical imaging analysis: A survey](https://mlsanity.com/paper/2109.08685/) (2021)
- [SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images](https://mlsanity.com/paper/2012.02383/) (2020)
- [PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation](https://mlsanity.com/paper/2011.12640/) (2020)
- [Instance-aware Self-supervised Learning for Nuclei Segmentation](https://mlsanity.com/paper/2007.11186/) (2020)
- [Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions](https://mlsanity.com/paper/1902.05655/) (2019)
- [Pyramid Medical Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2104.14702/) (2021)
- [Training Image Estimators without Image Ground-Truth](https://mlsanity.com/paper/1906.05775/) (2019)
- [ARPM-net: A novel CNN-based adversarial method with Markov Random Field enhancement for prostate and organs at risk segmentation in pelvic CT images](https://mlsanity.com/paper/2008.04488/) (2020)
- [Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images](https://mlsanity.com/paper/2009.09571/) (2020)
- [Weaving Attention U-net: A Novel Hybrid CNN and Attention-based Method for Organs-at-risk Segmentation in Head and Neck CT Images](https://mlsanity.com/paper/2107.04847/) (2021)

## Abstract (arXiv)

We developed a novel SSL approach to capture global consistency and pixel-level local consistencies between differently augmented views of the same images to accommodate downstream discriminative and dense predictive tasks. We adopted the teacher-student architecture used in previous contrastive SSL methods. In our method, the global consistency is enforced by aggregating the compressed representations of augmented views of the same image. The pixel-level consistency is enforced by pursuing similar representations for the same pixel in differently augmented views. Importantly, we introduced an uncertainty-aware context stabilizer to adaptively preserve the context gap created by the two views from different augmentations. Moreover, we used Monte Carlo dropout in the stabilizer to measure uncertainty and adaptively balance the discrepancy between the representations of the same pixels in different views.