Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images.

ABSTRACT: The success of chemotherapy and radiotherapy anti-cancer treatments can result in tumor suppression or senescence induction. Senescence was previously considered a favorable therapeutic outcome, until recent advancements in oncology research evidenced senescence as one of the culprits of cancer recurrence. Its detection requires multiple assays, and nonlinear optical (NLO) microscopy provides a solution for fast, non-invasive, and label-free detection of therapy-induced senescent cells. Here, we develop several deep learning architectures to perform binary classification between senescent and proliferating human cancer cells using NLO microscopy images and we compare their performances. As a result of our work, we demonstrate that the most performing approach is the one based on an ensembl

SUBMITTER: Sorrentino S 

PROVIDER: S-EPMC10326547 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature