  While self-supervised learning (SSL) algorithms have been widely used to
pre-train deep models, few efforts [11] have been done to improve
representation learning of X-ray image analysis with SSL pre-trained models. In
this work, we study a novel self-supervised pre-training pipeline, namely
Multi-task Self-super-vised Continual Learning (MUSCLE), for multiple medical
imaging tasks, such as classification and segmentation, using X-ray images
collected from multiple body parts, including heads, lungs, and bones.
Specifically, MUSCLE aggregates X-rays collected from multiple body parts for
MoCo-based representation learning, and adopts a well-designed continual
learning (CL) procedure to further pre-train the backbone subject various X-ray
analysis tasks jointly. Certain strategies for image pre-processing, learning
schedules, and regularization have been used to solve data heterogeneity,
overfitting, and catastrophic forgetting problems for multi-task/dataset
learning in MUSCLE.We evaluate MUSCLE using 9 real-world X-ray datasets with
various tasks, including pneumonia classification, skeletal abnormality
classification, lung segmentation, and tuberculosis (TB) detection. Comparisons
against other pre-trained models [7] confirm the proof-of-concept that
self-supervised multi-task/dataset continual pre-training could boost the
performance of X-ray image analysis.
