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

被引:1
|
作者
Sorrentino, Salvatore [1 ]
Manetti, Francesco [1 ]
Bresci, Arianna [1 ]
Vernuccio, Federico [1 ]
Ceconello, Chiara [1 ]
Ghislanzoni, Silvia [2 ]
Bongarzone, Italia [2 ]
Vanna, Renzo [3 ]
Cerullo, Giulio [1 ,3 ]
Polli, Dario [1 ,3 ]
机构
[1] Politecn Milan, Dept Phys, Milan, Italy
[2] Fdn IRCCS Ist Nazl Tumori Milano, Dept Adv Diagnost, Milan, Italy
[3] CNR Inst Photon & Nanotechnol CNR IFN, Milan, Italy
来源
FRONTIERS IN CHEMISTRY | 2023年 / 11卷
关键词
deep learning; transfer learning; ensemble learning; machine learning; neural networks; therapy-induced senescence; non-linear microscopy; multimodal imaging; CELLULAR SENESCENCE; IMPACT;
D O I
10.3389/fchem.2023.1213981
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
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收藏
页数:13
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