Noninvasive optical imaging modalities can probe patient's tissue in 3D and over time generate gigabytes of clinically relevant data per sample. There is a need for AI models to analyze this data and assist clinical workflow. The lack of expert labelers and the large dataset required (>100,000 images) for model training and tuning are the main hurdles in creating foundation models. In this paper we introduce FoundationShift, a method to apply any AI model from computational pathology without retraining. We show our method is more accurate than state of the art models (SAM, MedSAM, SAM-Med2D, CellProfiler, Hover-Net, PLIP, UNI and ChatGPT), with multiple imaging modalities (OCT and RCM). This is achieved without the need for model retraining or fine-tuning. Applying our method to noninvasive in vivo images could enable physicians to readily incorporate optical imaging modalities into their clinical practice, providing real time tissue analysis and improving patient care.

翻译：非侵入性光学成像模式能够在三维空间内探查患者组织，并随时间推移为每个样本生成千兆字节级的临床相关数据。亟需人工智能模型来分析这些数据并辅助临床工作流程。专家标注人员的缺乏以及模型训练与调优所需的大规模数据集（超过10万张图像）是创建基础模型的主要障碍。本文介绍了一种名为FoundationShift的方法，该方法能够直接应用计算病理学领域的任何人工智能模型而无需重新训练。我们证明，在多种成像模态（OCT和RCM）下，该方法比现有最先进模型（SAM、MedSAM、SAM-Med2D、CellProfiler、Hover-Net、PLIP、UNI和ChatGPT）更为准确。这一成果的取得无需模型重新训练或微调。将我们的方法应用于非侵入性活体成像，可使临床医生轻松地将光学成像模式整合到临床实践中，提供实时组织分析并改善患者护理。