Nature

Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging

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 ...
Nature

Evaluation of two semi-supervised learning methods and their combination for automatic classification of bone marrow cells

Bone marrow cell differential counting is a basic and important test for the diagnosis of various hematological diseases, such as myelodysplastic syndrome and leukemia 1,2,3. However, it requires a ...