Interpretability is critical in high-stakes domains such as medical imaging, where understanding model decisions is essential for clinical adoption. In this work, we introduce Sparse Autoencoder (SAE)-based interpretability to breast imaging by analyzing Mammo-CLIP, a vision–language foundation model pretrained on large-scale mammogram image–radiology report pairs. We train a patch-level Mammo-SAE on Mammo-CLIP visual features to identify and probe latent neurons associated with clinically relevant breast concepts such as mass and suspicious calcification. We show that top-activated class-level latent neurons often tend to align with ground-truth regions, and also uncover several confounding factors influencing the model’s decision-making process. Furthermore, we demonstrate that finetuning Mammo-CLIP leads to larger separation in the latent neuron space, improving interpretability and predictive performance. Our findings suggest that sparse latent representations offer a powerful lens into the internal behavior of breast foundation models.