\newpage
\section{Representation Level Analysis}
\textcolor{red}{%To provide the deeper analysis requested, we conducted t-SNE visualization of bottleneck features from 100 samples (67 benign, 33 malignant) on the BUS-BRA \cite{WilfridoGomezFlores.2023} dataset.  As can be seen in Figure ~\ref{fig:feature_level}, without NMB, benign and malignant features are heavily intermixed, indicating that the latent space lacks clear class structure. In contrast, when NMB is incorporated, features form more compact and class-aligned clusters with visibly reduced overlap (Fig. X, left), which is quantitatively reflected by a higher silhouette score (0.26 vs. 0.01). While t-SNE is primarily a visualization tool, the consistent improvement in both visual separability and clustering score suggests that NMB leads to more structured and class-discriminative representations. This representation-level behavior is consistent with the observed improvements in segmentation performance. 
\label{appendix:representation}
 \input{figures/tsne}
 }
 \section{Robustness to Context Selection Method}
\include{tables/context_method}
\label{appendix:context_sel}
\vspace{-0.25mm}
\section{Comparison with Few-Shot Learning Approaches and Feature-Wise Conditioning}
\vspace{-0.25mm}

\include{tables/few_shot_comparisons}
