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eess.IV2021
Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images
Zhiyang Lu, Zheng Li, Jun Wang +2
The thick-slice magnetic resonance (MR) images are often structurally blurred in coronal and sagittal views, which causes harm to diagnosis and image post-processing. Deep learning…
eess.IV2021
Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography
Ronglin Gong, Jun Wang, Jun Shi
Deep learning can promote the mammography-based computer-aided diagnosis (CAD) for breast cancers, but it generally suffers from the small sample size problem. Self-supervised lear…