13 citations · 22 across the 3 of their papers we have counts for
4 papers
QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems
Cassandra Tong Ye, Shamus Li, Tyler King +1
While deep learning offers tremendous promise for scientific and medical imaging, any failures and hallucinations (predictions that do not coincide with reality) are hard to pinpoi…
System- and Sample-agnostic Isotropic 3D Microscopy by Weakly Physics-informed, Domain-shift-resistant Axial Deblurring
Jiashu Han, Kunzan Liu, Keith B. Isaacson +3
Three-dimensional (3D) subcellular imaging is essential for biomedical research, but the diffraction limit of optical microscopy compromises axial resolution, hindering accurate 3D…
Learned, uncertainty-driven adaptive acquisition for photon-efficient scanning microscopy
Cassandra Tong Ye, Jiashu Han, Kunzan Liu +4
Scanning microscopy systems, such as confocal and multiphoton microscopy, are powerful imaging tools for probing deep into biological tissue. However, scanning systems have an inhe…
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian +72
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasin…