Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MR Videos
This work addresses the need for explainable injury detection in medical imaging, specifically for ACL tears in knee MR videos, but it is incremental as it applies existing self-supervised techniques to a new domain.
The paper tackles the problem of detecting ACL tear injuries in knee MR videos by proposing a self-supervised learning approach that learns transferable features from unlabeled data, achieving reliable and explainable performance in downstream classification tasks.
The success of deep learning based models for computer vision applications requires large scale human annotated data which are often expensive to generate. Self-supervised learning, a subset of unsupervised learning, handles this problem by learning meaningful features from unlabeled image or video data. In this paper, we propose a self-supervised learning approach to learn transferable features from MR video clips by enforcing the model to learn anatomical features. The pretext task models are designed to predict the correct ordering of the jumbled image patches that the MR video frames are divided into. To the best of our knowledge, none of the supervised learning models performing injury classification task from MR video provide any explanation for the decisions made by the models and hence makes our work the first of its kind on MR video data. Experiments on the pretext task show that this proposed approach enables the model to learn spatial context invariant features which help for reliable and explainable performance in downstream tasks like classification of Anterior Cruciate Ligament tear injury from knee MRI. The efficiency of the novel Convolutional Neural Network proposed in this paper is reflected in the experimental results obtained in the downstream task.