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13 March 2024 Deep ensemble-transfer learning for the classification of senescent cells from Non-linear optical microscopy images
Salvatore Sorrentino, Francesco Manetti, Arianna Bresci, Federico Vernuccio, Chiara Ceconello, Marco Ventura, Andrea Rabolini, Silvia Ghislanzoni, Italia Bongarzone, Renzo Vanna, Giulio Cerullo, Dario Polli
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Proceedings Volume PC12834, Multimodal Biomedical Imaging XIX; PC128340B (2024) https://doi.org/10.1117/12.2691639
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Image classification using Deep Ensemble Learning and Transfer Learning methods is performed on a small, labeled dataset of multimodal nonlinear optical microscopy images coming from Stimulated Raman Scattering, Two Photon Excited Fluorescence and Optical Transmission, to differentiate proliferating cancer cells from senescent ones, a peculiar phenotype following an anti-cancer treatment responsible for tumour relapse. The superior performances of the Deep Ensemble Transfer Learning approach are compared with other less complex neural network architectures. Ultimately, the predictions of the neural network are evaluated using the Grad-CAM visualization approach, which allows highlighting the most important features in the input images responsible for the labels assigned by the network.
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Salvatore Sorrentino, Francesco Manetti, Arianna Bresci, Federico Vernuccio, Chiara Ceconello, Marco Ventura, Andrea Rabolini, Silvia Ghislanzoni, Italia Bongarzone, Renzo Vanna, Giulio Cerullo, and Dario Polli "Deep ensemble-transfer learning for the classification of senescent cells from Non-linear optical microscopy images", Proc. SPIE PC12834, Multimodal Biomedical Imaging XIX, PC128340B (13 March 2024); https://doi.org/10.1117/12.2691639
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KEYWORDS
Nonlinear optics
Image classification
Cancer
Optical microscopy
Oncology
Deep learning
Cancer detection
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