Understanding the temporal evolution of cells poses a significant challenge in developmental biology. This study embarks on a comparative analysis of various machine-learning techniques to classify cell colony images across different timestamps, thereby aiming to capture dynamic transitions of cellular states. By performing Transfer Learning with state-of-the-art classification networks, we achieve high accuracy in categorizing single-timestamp images. Furthermore, this research introduces the integration of temporal models, notably LSTM (Long Short Term Memory Network), R-Transformer (Recurrent Neural Network enhanced Transformer) and ViViT (Video Vision Transformer), to undertake this classification task to verify the effectiveness of incorporating temporal features into the classification through a comprehensive comparative analysis of these models compare to non-temporal models. This investigation not only benchmarks the efficacy of different machine-learning approaches in understanding cellular forms but also sets a precedent for future research aimed at enriching our comprehension of cellular developments through enhanced computational methodologies. The insights and methodologies derived from this study promise to contribute significantly to the advancement of computational techniques in the realm of biological research, paving the way for deeper insights into the intricacies of cellular behavior and evolution.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-4452 |
| Date | 01 March 2024 |
| Creators | Zhao, Xinran |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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