# BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net models

arXiv: https://arxiv.org/abs/2112.03916 · HTML: https://mlsanity.com/paper/2112.03916/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 37 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Builds on (most cited first, 27 of 27)

- [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)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (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)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [Medical Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2009.13120/) (2020)
- [Positional Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2106.09157/) (2021)
- [ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection](https://mlsanity.com/paper/1703.00523/) (2017)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation](https://mlsanity.com/paper/2107.04886/) (2021)
- [RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging](https://mlsanity.com/paper/2108.02508/) (2021)
- [Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations](https://mlsanity.com/paper/2103.04167/) (2021)
- [Self-Supervised Learning for Segmentation](https://mlsanity.com/paper/2101.05456/) (2021)

## Built on by (most cited first, 12 of 12)

- [Multi-modal Vision Pre-training for Medical Image Analysis](https://mlsanity.com/paper/2410.10604/) (2024)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [Dense Self-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2407.20395/) (2024)
- [Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation](https://mlsanity.com/paper/2409.20332/) (2024)
- [Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training](https://mlsanity.com/paper/2402.11497/) (2024)
- [Resource Efficient Multi-stain Kidney Glomeruli Segmentation via Self-supervision](https://mlsanity.com/paper/2412.15389/) (2024)
- [MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation](https://mlsanity.com/paper/2303.12130/) (2023)
- [BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2308.16819/) (2023)
- [Deep Learning-Assisted Detection of Sarcopenia in Cross-Sectional Computed Tomography Imaging](https://mlsanity.com/paper/2508.17275/) (2025)
- [Barlow-Swin: Toward a novel siamese-based segmentation architecture using Swin-Transformers](https://mlsanity.com/paper/2509.06885/) (2025)
- [The ecosystem of machine learning competitions: Platforms, participants, and their impact on AI development](https://mlsanity.com/paper/2604.08001/) (2026)
- [Pix2Rep-v2: Data-Efficient Representation Learning for Dense Medical Imaging Applications](https://mlsanity.com/paper/2609.01427/) (2026)

## Abstract (arXiv)

Deep learning has brought the most profound contribution towards biomedical image segmentation to automate the process of delineation in medical imaging. To accomplish such task, the models are required to be trained using huge amount of annotated or labelled data that highlights the region of interest with a binary mask. However, efficient generation of the annotations for such huge data requires expert biomedical analysts and extensive manual effort. It is a tedious and expensive task, while also being vulnerable to human error. To address this problem, a self-supervised learning framework, BT-Unet is proposed that uses the Barlow Twins approach to pre-train the encoder of a U-Net model via redundancy reduction in an unsupervised manner to learn data representation. Later, complete network is fine-tuned to perform actual segmentation. The BT-Unet framework can be trained with a limited number of annotated samples while having high number of unannotated samples, which is mostly the case in real-world problems. This framework is validated over multiple U-Net models over diverse datasets by generating scenarios of a limited number of labelled samples using standard evaluation metrics. With exhaustive experiment trials, it is observed that the BT-Unet framework enhances the performance of the U-Net models with significant margin under such circumstances.