# SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality Classification from MRI

arXiv: https://arxiv.org/abs/2208.13923 · HTML: https://mlsanity.com/paper/2208.13923/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 29 Aug 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [A Survey of Visual Transformers](https://mlsanity.com/paper/2111.06091/) (2021)
- [Medical Image Analysis using Convolutional Neural Networks: A Review](https://mlsanity.com/paper/1709.02250/) (2017)
- [SiT: Self-supervised vIsion Transformer](https://mlsanity.com/paper/2104.03602/) (2021)
- [Masked Image Modeling Advances 3D Medical Image Analysis](https://mlsanity.com/paper/2204.11716/) (2022)
- [GMML is All you Need](https://mlsanity.com/paper/2205.14986/) (2022)
- [MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning](https://mlsanity.com/paper/2111.15340/) (2021)
- [Knee Injury Detection using MRI with Efficiently-Layered Network (ELNet)](https://mlsanity.com/paper/2005.02706/) (2020)
- [Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MR Videos](https://mlsanity.com/paper/2007.07761/) (2020)
- [Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI](https://mlsanity.com/paper/2009.03128/) (2020)
- [Deep Learning for Musculoskeletal Image Analysis](https://mlsanity.com/paper/2003.00541/) (2020)

## Built on by (most cited first, 6 of 6)

- [ASiT: Local-Global Audio Spectrogram vIsion Transformer for Event Classification](https://mlsanity.com/paper/2211.13189/) (2022)
- [SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model](https://mlsanity.com/paper/2211.12944/) (2022)
- [DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies](https://mlsanity.com/paper/2402.15534/) (2024)
- [LT-ViT: A Vision Transformer for multi-label Chest X-ray classification](https://mlsanity.com/paper/2311.07263/) (2023)
- [A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images](https://mlsanity.com/paper/2309.02555/) (2023)
- [SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks](https://mlsanity.com/paper/2510.19829/) (2025)

## Abstract (arXiv)

The availability of large scale data with high quality ground truth labels is a challenge when developing supervised machine learning solutions for healthcare domain. Although, the amount of digital data in clinical workflows is increasing, most of this data is distributed on clinical sites and protected to ensure patient privacy. Radiological readings and dealing with large-scale clinical data puts a significant burden on the available resources, and this is where machine learning and artificial intelligence play a pivotal role. Magnetic Resonance Imaging (MRI) for musculoskeletal (MSK) diagnosis is one example where the scans have a wealth of information, but require a significant amount of time for reading and labeling. Self-supervised learning (SSL) can be a solution for handling the lack of availability of ground truth labels, but generally requires a large amount of training data during the pretraining stage. Herein, we propose a slice-based self-supervised deep learning framework (SB-SSL), a novel slice-based paradigm for classifying abnormality using knee MRI scans. We show that for a limited number of cases (<1000), our proposed framework is capable to identify anterior cruciate ligament tear with an accuracy of 89.17% and an AUC of 0.954, outperforming state-of-the-art without usage of external data during pretraining. This demonstrates that our proposed framework is suited for SSL in the limited data regime.