Assad A. Oberai

This paper introduces a feature-preserving conditional GAN for multi-site MRI harmonization, showing improved performance over existing methods.

This paper compares probabilistic generative models for MR image transformation, finding NCSN most accurate and DDPM best at detecting out-of-distribution inputs.

This paper introduces an automated method to detect rare events in liquid biopsies without prior knowledge of their signatures, demonstrating its effectiveness on immunofluorescence microscopy data.

This paper introduces a contrastive learning framework for single-cell phenotyping in whole slide imaging of liquid biopsy samples, achieving high accuracy and enabling automated identification of rare cell phenotypes.

This study trains a conditional generative adversarial network to estimate tumor spheroid elasticity from phase difference images, showing improved robustness to noise compared to traditional methods.