Out-of-distribution (OOD) data poses serious challenges in deployed mach...
Out-of-distribution (OOD) data poses serious challenges in deployed mach...
Deep learning methods, in particular convolutional neural networks, have...
The quantitative detection, segmentation, and characterization of glomer...
Leveraging machine learning to optimize the optimization process is an
e...
Recent studies have demonstrated the diagnostic and prognostic values of...
Learning from set-structured data is an essential problem with many
appl...
Box representation has been extensively used for object detection in com...
We present an approach for compressing volumetric scalar fields using
im...
Contrastive learning is a key technique of modern self-supervised learni...
Annotated medical images are typically rarer than labeled natural images...
The classification of glomerular lesions is a routine and essential task...
Considerable morphological phenotyping studies in nephrology have emerge...
There has been a long pursuit for precise and reproducible glomerular
qu...
Object detection networks are powerful in computer vision, but not
neces...