无需重训练，直接复用病理AI分析光学影像。
Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining
- 将病理AI模型迁移至光学影像，无需重新训练
- 在OCT和RCM上优于现有10种主流模型
- 适合希望快速落地光学影像分析的临床团队
非侵入性光学成像可三维、动态地获取患者组织数据，每样本产生数GB临床相关数据。亟需AI模型分析此类数据以支持临床工作流。然而，专家标注稀缺及模型训练所需超10万张图像的数据量，是构建基础模型的主要障碍。本文提出FoundationShift方法，可直接应用计算病理学中的任意AI模型，无需重训练或微调。实验表明，该方法在多种成像模态（OCT与RCM）下表现优于当前最优模型（SAM、MedSAM、SAM-Med2D、CellProfiler、Hover-Net、PLIP、UNI及ChatGPT）。该技术可使医生便捷地将光学成像纳入临床实践，实现实时组织分析，提升患者诊疗质量。
原文摘要 · Abstract (English)
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.
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