79 citations · 187 across the 15 of their papers we have counts for
3 papers · 1 filter
Adaptive Correspondence Scoring for Unsupervised Medical Image Registration
Xiaoran Zhang, John C. Stendahl, Lawrence Staib +3
We propose an adaptive training scheme for unsupervised medical image registration. Existing methods rely on image reconstruction as the primary supervision signal. However, nuisan…
Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration
Xiaoran Zhang, Daniel H. Pak, Shawn S. Ahn +6
Deep learning methods for unsupervised registration often rely on objectives that assume a uniform noise level across the spatial domain (e.g. mean-squared error loss), but noise d…
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
Chenyu You, Weicheng Dai, Yifei Min +5
For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pa…