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.

我们首次初步研究了类似解释能力方法在医学成像领域的应用，使用一个ViT-Small编码器对病理基金模型的特征进行分析，并在两个数据集上进行了应用：一个是病理图像数据集，另一个是与空间转录组学配对的病理图像数据集。我们在模型嵌入空间中发现了一种可解释的细胞和组织形态以及基因表达的表示方式。我们的研究为进一步探索可解释特征维度及其在医学和临床应用中的实用性铺平了道路。

对一种基于病理学模型的可解释性分析揭示了跨模态的生物相关嵌入