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

Content Provider	Europe PMC
Author	Sorrentino, Salvatore Manetti, Francesco Bresci, Arianna Vernuccio, Federico Ceconello, Chiara Ghislanzoni, Silvia Bongarzone, Italia Vanna, Renzo Cerullo, Giulio Polli, Dario
Copyright Year	2023
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 ensemble classifier, that uses seven different pre-trained classification networks, taken from literature, with the addition of fully connected layers on top of their architectures. This approach achieves a classification accuracy of over 90%, showing the possibility of building an automatic, unbiased senescent cells image classifier starting from multimodal NLO microscopy data. Our results open the way to a deeper investigation of senescence classification via deep learning techniques with a potential application in clinical diagnosis.
Journal	Frontiers in Chemistry
Volume Number	11
PubMed Central reference number	PMC10326547
PubMed reference number	37426334
e-ISSN	22962646
DOI	10.3389/fchem.2023.1213981
Language	English
Publisher	Frontiers Media S.A.
Publisher Date	2023-06-23
Access Restriction	Open
Rights License	This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Copyright © 2023 Sorrentino, Manetti, Bresci, Vernuccio, Ceconello, Ghislanzoni, Bongarzone, Vanna, Cerullo and Polli.
Subject Keyword	deep learning transfer learning ensemble learning machine learning neural networks therapy-induced senescence non-linear microscopy multimodal imaging
Content Type	Text
Resource Type	Article
Subject	Chemistry