\begin{figure*}[!ht]
\centering
\includegraphics[width=1.0\columnwidth]{figures/Intra_Inter_Subject_v3.pdf}
\vspace{-1.6\baselineskip}
\caption{\textbf{Intra-Subject and Inter-Subject Analysis.} 
We visualize an example image for intra-subject (A) and inter-subject (B) analyses using equivariant learning (SRENet and E2CNN) and standard convolution (ResNet). The TMA image undergoes 30-degree rotation increments (top row). For each rotation angle, the resulting segmentation after unsupervised K-means clustering was plotted and then unrotated back to the original input orientation for each model. Because the K-means cluster fitting was performed independently, the segmentation label colormap is not consistent across models.}
\label{fig:intraintersubject}
\end{figure*}

