no code implementations • 23 Feb 2021 • Alvaro Gomariz, Tiziano Portenier, César Nombela-Arrieta, Orcun Goksel
We herein propose a deep learning-based cell detection framework that can operate on large microscopy images and outputs desired probabilistic predictions by (i) integrating Bayesian techniques for the regression of uncertainty-aware density maps, where peak detection can be applied to generate cell proposals, and (ii) learning a mapping from the numerous proposals to a probabilistic space that is calibrated, i. e. accurately represents the chances of a successful prediction.
no code implementations • 27 Jan 2021 • Alvaro Gomariz, Raphael Egli, Tiziano Portenier, César Nombela-Arrieta, Orcun Goksel
However, for combinations that do not exist in a labeled training dataset, one cannot have any estimation of potential segmentation quality if that combination is encountered during inference.
no code implementations • 27 Aug 2020 • Alvaro Gomariz, Tiziano Portenier, Patrick M. Helbling, Stephan Isringhausen, Ute Suessbier, César Nombela-Arrieta, Orcun Goksel
Quantitative characterization of structures in acquired images often relies on automatic image analysis methods.