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.

翻译：基于深度学习的方法现已在生物物理学领域广泛应用，用于自动化多种任务，包括图像分割、特征选择和去卷积。然而，多种相互竞争的深度学习架构并存，每种架构都有其独特的优势和不足，这使得为特定应用选择最合适的架构具有挑战性。为此，我们提出了常见模型的全面比较。本文聚焦于分割任务，假设训练数据集规模通常较小（这是生物物理实验中常见的情况），并比较了以下四种常用架构：卷积神经网络、U-Net、视觉Transformer和视觉状态空间模型。通过这一比较，我们建立了确定每种模型表现最优条件的标准，从而为该领域的研究人员和实践者提供了实用指南。