SSLM: Self-Supervised Learning for Medical Diagnosis from MR Video
2021
In medical image analysis, the cost of acquiring high-quality data and their
annotation by experts is a barrier in many medical applications. Most of the
techniques used are based on supervised learning framework and need a large
amount of annotated data to achieve satisfactory performance. As an
alternative, in this paper, we propose a self-supervised learning approach to
learn the spatial anatomical representations from the frames of magnetic
resonance (MR) video clips for the diagnosis of knee medical conditions. The
pretext model learns meaningful spatial context-invariant representations. The
downstream task in our paper is a class imbalanced multi-label classification.
Different experiments show that the features learnt by the pretext model
provide explainable performance in the downstream task. Moreover, the
efficiency and reliability of the proposed pretext model in learning
representations of minority classes without applying any strategy towards
imbalance in the dataset can be seen from the results. To the best of our
knowledge, this work is the first work of its kind in showing the effectiveness
and reliability of self-supervised learning algorithms in class imbalanced
multi-label classification tasks on MR video. The code for evaluation of the proposed work is available at
https://github.com/anonymous-cvpr/sslm
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