SAM$^{*}$: Task-Adaptive SAM with Physics-Guided Rewards
This addresses the usability challenge of foundational segmentation models for real-time microscopy analysis, though it appears incremental as it builds on the existing SAM framework.
The paper tackles the problem of foundational models like SAM having too many opaque tuning parameters that hinder real-time streaming data analysis in microscopy image segmentation, by introducing a reward function-based optimization approach that incorporates physics-based criteria to create SAM*, resulting in improved adaptability and performance for real-time segmentation tasks.
Image segmentation is a critical task in microscopy, essential for accurately analyzing and interpreting complex visual data. This task can be performed using custom models trained on domain-specific datasets, transfer learning from pre-trained models, or foundational models that offer broad applicability. However, foundational models often present a considerable number of non-transparent tuning parameters that require extensive manual optimization, limiting their usability for real-time streaming data analysis. Here, we introduce a reward function-based optimization to fine-tune foundational models and illustrate this approach for SAM (Segment Anything Model) framework by Meta. The reward functions can be constructed to represent the physics of the imaged system, including particle size distributions, geometries, and other criteria. By integrating a reward-driven optimization framework, we enhance SAM's adaptability and performance, leading to an optimized variant, SAM$^{*}$, that better aligns with the requirements of diverse segmentation tasks and particularly allows for real-time streaming data segmentation. We demonstrate the effectiveness of this approach in microscopy imaging, where precise segmentation is crucial for analyzing cellular structures, material interfaces, and nanoscale features.