Real-time margin assessment in breast cancer surgeries is critical to reduce positive margin rates. The iKnife is an intra-operative modality that captures the molecular signature of tissues and can be paired with AI to facilitate real-time tissue characterization. As training these AI models is typically done with homogeneous ex-vivo iKnife data, intra-operative deployment is challenging because of tissue heterogeneity and unseen classes. In this study, we explore different mechanisms to address the intra-operative deployment challenge. Using cross validation and comparison to baseline methods, we show that the intermediate attention of graph transformer model as well as the uncertainty estimation of Bayesian neural network can be used to to reduce false positive rate of breast cancer surgery. We conclude that the class prediction output is not enough for successful deployment and additional interpretability features are needed to improve the performance.