# Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation

arXiv: https://arxiv.org/abs/2207.11191 · HTML: https://mlsanity.com/paper/2207.11191/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 43 references that are in the map.
- First posted on arXiv 17 Jul 2022.

## Builds on (most cited first, 25 of 25)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Masked Feature Prediction for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2112.09133/) (2021)
- [Rethinking ImageNet Pre-training](https://mlsanity.com/paper/1811.08883/) (2018)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [Does Knowledge Distillation Really Work?](https://mlsanity.com/paper/2106.05945/) (2021)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- [Self-Supervised Learning of Object Parts for Semantic Segmentation](https://mlsanity.com/paper/2204.13101/) (2022)
- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation](https://mlsanity.com/paper/2203.10095/) (2022)
- [Self-Supervised Feature Learning by Learning to Spot Artifacts](https://mlsanity.com/paper/1806.05024/) (2018)
- [Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching](https://mlsanity.com/paper/2106.08600/) (2021)
- [QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy](https://mlsanity.com/paper/1801.04161/) (2018)
- [Federated Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2204.10983/) (2022)
- [A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis](https://mlsanity.com/paper/2108.05930/) (2021)
- [Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation](https://mlsanity.com/paper/2007.05363/) (2020)
- [Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models](https://mlsanity.com/paper/2104.02481/) (2021)
- [Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation](https://mlsanity.com/paper/2107.04914/) (2021)
- [Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)](https://mlsanity.com/paper/2107.12889/) (2021)

## Built on by (most cited first, 7 of 7)

- [Self-Supervised Learning for Image Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2505.13584/) (2025)
- [Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methods](https://mlsanity.com/paper/2408.03393/) (2024)
- [CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation](https://mlsanity.com/paper/2310.03981/) (2023)
- [Application Of Vision-Language Models For Assessing Osteoarthritis Disease Severity](https://mlsanity.com/paper/2401.06331/) (2024)
- [Self-supervised TransUNet for Ultrasound regional segmentation of the distal radius in children](https://mlsanity.com/paper/2309.09490/) (2023)
- [A Simple Framework Uniting Visual In-context Learning with Masked Image Modeling to Improve Ultrasound Segmentation](https://mlsanity.com/paper/2402.14300/) (2024)
- [FlexICL: A Flexible Visual In-context Learning Framework for Elbow and Wrist Ultrasound Segmentation](https://mlsanity.com/paper/2510.26049/) (2025)

## Abstract (arXiv)

Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is time-consuming and expensive, especially for segmentation tasks. To solve the problem of learning with limited labeled medical image data, an alternative deep learning training strategy based on self-supervised pretraining on unlabeled MRI scans is proposed in this work. Our pretraining approach first, randomly applies different distortions to random areas of unlabeled images and then predicts the type of distortions and loss of information. To this aim, an improved version of Mask-RCNN architecture has been adapted to localize the distortion location and recover the original image pixels. The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated based on the Osteoarthritis Initiative dataset. Using this self-supervised pretraining method improved the Dice score by 20% compared to training from scratch. The proposed self-supervised learning is simple, effective, and suitable for different ranges of medical image analysis tasks including anomaly detection, segmentation, and classification.