Optimizing Deep Learning Models for Cell Recognition in Fluorescence Microscopy: The Impact of Loss Functions on Performance and Generalization

In the rapidly evolving domain of fluorescence microscopy, the application of Deep Learning techniques for automatic cell segmentation presents exciting opportunities and challenges. In this work, we investigate the impact of loss functions and evaluation metrics on model performance and generalization in the context of cell recognition. First, we present extensive experiments with different commonly used loss functions and offer practical insights and guidelines, underscoring how the choice of a loss function can influence model performance. Second, we conduct a detailed examination of several evaluation metrics with their relative benefits and drawbacks, helping to guide effective model evaluation and comparison in the field. Third, we discuss how characteristics specific to fluorescence microscopy data impact model generalization. Precisely, we examine how factors such as cell sizes, color irregularities, and textures can potentially affect the performance and adaptability of these models to new data. Collectively, these insights provide an understanding of the various facets resulting from the application of Deep Learning for automatic cell segmentation, shedding light on best practices, evaluation strategies, and model generalization. Hence, this study can serve as a beneficial resource for researchers and practitioners working on similar applications, fostering further advancements in the field.