Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

This article reviews deep learning applications in biomedical optics with a particular
emphasis on image formation. The review is organized by imaging domains within …

Existing approaches to evaluate cell viability involve cell staining with chemical reagents.
However, the step of exogenous staining makes these methods undesirable for rapid …

Traditional imaging cytometry uses fluorescence markers to identify specific structures but is
limited in throughput by the labeling process. We develop a label-free technique that …

In medical imaging, deep learning-based solutions have achieved state-of-the-art
performance. However, reliability restricts the integration of deep learning into practical …

We report herein the first visible light optical coherence tomography angiography (vis-OCTA)
for human retinal imaging. Compared to the existing vis-OCT systems, we devised a …

Background and objective Human hemodynamic modeling is usually influenced by
uncertainties occurring from a considerable unavailability of information linked to the …

The solution of an inverse problem in computational imaging (CI) often requires the
knowledge of the physical model and/or the object. However, in many practical applications …

Supervised data are not always uncontroversial, especially for boundary detection tasks.
Considering that we have a portrait, the face contour is naturally the most salient boundary …

Deep unrolling is an emerging deep learning-based image reconstruction methodology that
bridges the gap between model-based and purely deep learning-based image …