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

Area of Science:

• Oncology
• Biomedical Imaging
• Artificial Intelligence

Background:

• Chemotherapy and radiotherapy can induce cellular senescence, a process implicated in cancer recurrence.
• Current methods for detecting senescent cells are often time-consuming and invasive.
• Nonlinear optical (NLO) microscopy offers a label-free, rapid imaging approach for senescent cell identification.

Purpose of the Study:

• To develop and compare deep learning architectures for classifying senescent versus proliferating human cancer cells.
• To leverage NLO microscopy images for automated and unbiased senescent cell detection.
• To assess the potential of deep learning in clinical diagnosis of therapy-induced senescence.

Main Methods:

• Development of multiple deep learning architectures for image classification.
• Utilizing multimodal NLO microscopy images as input data.
• Implementation of an ensemble classifier combining seven pre-trained networks with added fully connected layers.

Main Results:

• The ensemble classifier achieved over 90% accuracy in distinguishing senescent from proliferating cancer cells.
• Demonstrated the feasibility of an automated, unbiased image classification system for senescent cells.
• Validated the effectiveness of deep learning models in analyzing NLO microscopy data.

Conclusions:

• Deep learning, particularly ensemble methods, shows significant promise for accurate senescent cell classification using NLO microscopy.
• This approach can facilitate faster and more objective detection of therapy-induced senescence.
• The findings pave the way for potential applications in clinical cancer diagnosis and treatment monitoring.