Mechanistic interpretability has been explored in detail for large language models (LLMs). For the first time, we provide a preliminary investigation with similar interpretability methods for medical imaging. Specifically, we analyze the features from a ViT-Small encoder obtained from a pathology Foundation Model via application to two datasets: one dataset of pathology images, and one dataset of pathology images paired with spatial transcriptomics. We discover an interpretable representation of cell and tissue morphology, along with gene expression within the model embedding space. Our work paves the way for further exploration around interpretable feature dimensions and their utility for medical and clinical applications.

翻译：机制可解释性已在大型语言模型（LLMs）中得到详细探索。我们首次将类似的可解释性方法初步应用于医学影像分析。具体而言，我们通过将病理学基础模型中的ViT-Small编码器特征应用于两个数据集进行分析：一个病理图像数据集，以及一个病理图像与空间转录组学配对的数据集。我们在模型嵌入空间中发现了一种可解释的细胞与组织形态学表征，以及基因表达信息。本研究为进一步探索可解释特征维度及其在医学与临床应用中的效用奠定了基础。