Fluorescence lifetime image microscopy prediction with convolutional neural networks for cell detection and classification in tissues.

ABSTRACT: Convolutional neural networks (CNNs) and other deep-learning models have proven to be transformative tools for the automated analysis of microscopy images, particularly in the domain of cellular and tissue imaging. These computer-vision models have primarily been applied with traditional microscopy imaging modalities (e.g. brightfield and fluorescence), likely due to the availability of large datasets in these regimes. However, more advanced microscopy imaging techniques could, potentially, allow for improved model performance in various computational histopathology tasks. In this work, we demonstrate that CNNs can achieve high accuracy in cell detection and classification without large amounts of data when applied to histology images acquired by fluorescence lifetime imaging microscopy (F

SUBMITTER: Smolen JA 

PROVIDER: S-EPMC9802238 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature