Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging. (arXiv:2308.06534v4 [cs.CV] UPDATED)

Deep learning in medical imaging has the potential to minimize the risk of
diagnostic errors, reduce radiologist workload, and accelerate diagnosis.
Training such deep learning models requires large and accurate datasets, with
annotations for all training samples. However, in the medical imaging domain,
annotated datasets for specific tasks are often small due to the high
complexity of annotations, limited access, or the rarity of diseases. To
address this challenge, deep learning models can be pre-trained on large image
datasets without …

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