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

• URL: http://arxiv.org/abs/2208.13923v1
• Date: Mon, 29 Aug 2022 23:08:41 GMT
• Title: SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality Classification from MRI
• Authors: Sara Atito, Syed Muhammad Anwar, Muhammad Awais, Josef Kitler
• Abstract summary: We propose a slice-based self-supervised deep learning framework (SBSSL) for classifying abnormality using knee MRI scans. 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.
• Score: 5.199134881541244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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