activity
20242026
collaborators

7 papers

cs.CV2026

Motion-guided sparse correction enables expert-quality point tracking across diverse microscopy regimes

Leonidas Zimianitis, Pasindu Thenahandi, Kai Buckhalter +10

Tracking the dynamics of non-canonical biological systems in microscopy videos remains a persistent challenge. Both classical and learning-based trackers depend on expert-reviewed…

physics.optics2026

High-Sensitivity, High-Throughput Double Sagnac Lateral Shearing Quantitative Phase Microscopy and Tomography with Pseudo-Thermal Illumination

Pawel Goclowski, Maciej Trusiak, Balpreet S. Ahluwalia +1

Quantitative phase microscopy (QPM) enables label-free measurement of local optical path length variations, providing critical insight into the structure and dynamics of transparen…

physics.optics2026

Lateral shearing optical diffraction tomography of brain organoid with reduced spatial coherence

Pawel Goclowski, Julianna Winnik, Vishesh Dubey +6

Optical diffraction tomography (ODT) is a powerful technique for quantitative, label-free reconstruction of the three-dimensional refractive index (RI) distribution of biological s…

physics.optics2026

High-Fidelity Single-Shot Quantitative Differential Phase Microscopy Using Pseudothermal Sagnac Interferometer

Pawel Goclowski, Hong Mao, Maciek Trusiak +2

In this letter, a high-fidelity single-shot differential quantitative phase microscopy (dQPM) method is presented to effectively image nearly transparent biological samples. The pr…

physics.optics2025

Bayesian inference for precise and uncertainty-quantified single-shot widefield interferometric geometrical nanometrology

Damian Suski, Maria Cywinska, Julianna Winnik +5

Advanced geometrical nanometrology is critical for process control in semiconductor manufacturing, supporting applications in, e.g., photonic integrated circuits, nanoelectronics,…

cs.LG2025

Thinking in Groups: Permutation Tests Reveal Near-Out-of-Distribution

Yasith Jayawardana, Dineth Jayakody, Sampath Jayarathna +1

Deep neural networks (DNNs) have the potential to power many biomedical workflows, but training them on truly representative, IID datasets is often infeasible. Most models instead…