Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data
This work addresses the problem of architecture selection for researchers and practitioners in biophysics, offering incremental insights through comparative analysis.
The paper tackles the challenge of selecting appropriate deep learning architectures for segmentation tasks in biophysics by comparing four models—convolutional neural networks, U-Nets, vision transformers, and vision state space models—under small training dataset conditions, establishing criteria for optimal model performance to provide practical guidelines.
Deep learning based approaches are now widely used across biophysics to help automate a variety of tasks including image segmentation, feature selection, and deconvolution. However, the presence of multiple competing deep learning architectures, each with its own unique advantages and disadvantages, makes it challenging to select an architecture best suited for a specific application. As such, we present a comprehensive comparison of common models. Here, we focus on the task of segmentation assuming the typically small training dataset sizes available from biophysics experiments and compare the following four commonly used architectures: convolutional neural networks, U-Nets, vision transformers, and vision state space models. In doing so, we establish criteria for determining optimal conditions under which each model excels, thereby offering practical guidelines for researchers and practitioners in the field.