Quantitative Imaging & Informatics Laboratory pursues excellence and integrity in
research and education. Through the computational techniques and knowledge, we dare to challenge and
resolve real-world problems.
A paper "Improving dense pixelwise prediction of epithelial density using unsupervised
data augmentation for consistency regularization" has been accepted for presentation at