# DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations
## 4 Experiment
### Learning Curve Analysis  
To further investigate the impact of annotated data scarcity on model performance, we conducted a learning curve analysis using varying proportions of labeled data, as illustrated in Figures 8 and 9. Following the suggestion in [45], we estimated classification performance bounds by progressively increasing the percentage of labeled samples from 5% to 20%. The results consistently demonstrate that the AUC-ROC and Macro F1 scores improve with  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Training Time/Epoch (s)** & **Peak GPU Memory (MB)** & **Relative Time Overhead** & **Relative Memory Overhead** \\ \hline SimCLR & 85.3 & 8100 & 1.00\(\times\) & 1.00\(\times\) \\ BYOL & 91.7 & 8250 & 1.08\(\times\) & 1.02\(\times\) \\ ViT & 96.5 & 8600 & 1.13\(\times\) & 1.06\(\times\) \\ CoAtNet & 102.1 & 9200 & 1.20\(\times\) & 1.14\(\times\) \\ DenTNet & 98.4 & 9100 & 1.15\(\times\) & 1.12\(\times\) \\ MedWT & 87.2 & 8400 & 1.02\(\times\) & 1.04\(\times\) \\ EffNetV2 & 83.9 & 7950 & 0.98\(\times\) & 0.98\(\times\) \\ Swin-UNETR & 107.6 & 9400 & 1.26\(\times\) & 1.16\(\times\) \\ MLFF & 111.2 & 9800 & 1.30\(\times\) & 1.21\(\times\) \\
**DCS-ST** & **118.5** & **10,600** & **1.39\(\times\)** & **1.31\(\times\)** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Empirical Computational Complexity of Models on the BreakHis Dataset (Evaluated on NVIDIA T4 \(\times\)2 GPUs).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Balanced Accuracy (Mean \(\pm\) SD)** & **F1 Score (Mean \(\pm\) SD)** & _p-Value (BA)_ & _p-Value (F1)_ & **Significant** \\ \hline SimCLR & 0.7385 \(\pm\) 0.0204 & 0.8412 \(\pm\) 0.0237 & \(<\)0.0001 & \(<\)0.0001 & ✓ \\ BYOL & 0.7720 \(\pm\) 0.0509 & 0.8643 \(\pm\) 0.0180 & \(<\)0.0001 & \(<\)0.0001 & ✓ \\ ViT & 0.7673 \(\pm\) 0.0289 & 0.8717 \(\pm\) 0.0160 & \(<\)0.0001 & \(<\)0.0001 & ✓ \\ CoAtNet & 0.8263 \(\pm\) 0.0099 & 0.8790 \(\pm\) 0.0225 & \(<\)0.0001 & \(<\)0.0001 & ✓ \\ DenTNet & 0.8131 \(\pm\) 0.0301 & 0.8226 \(\pm\) 0.0198 & \(<\)0.0001 & \(<\)0.0001 & ✓ \\ MedViT & 0.7445 \(\pm\) 0.0257 & 0.8270 \(\pm\) 0.0428 & \(<\)0.0001 & \(<\)0.0001 & ✓ \\ EffNetV2 & 0.7710 \(\pm\) 0.0179 & 0.7897 \(\pm\) 0.0479 & \(<\)0.0001 & \(<\)0.0001 & ✓ \\ Swin-UNETR & 0.8589 \(\pm\) 0.0276 & 0.8771 \(\pm\) 0.0103 & 0.0002 & \(<\)0.0001 & ✓ \\ MLFF & 0.8469 \(\pm\) 0.0276 & 0.8675 \(\pm\) 0.0226 & \(<\)0.0001 & \(<\)0.0001 & ✓ \\
**DCS-ST** & **0.8674 \(\pm\) 0.0256** & **0.9091 \(\pm\) 0.0204** & – & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 9: Statistical significance test between DCS-ST and baseline models on the BreakHis dataset (average over 4 magnifications, 5 simulated runs).  
Figure 7: Comparison of the confusion matrices of the Mini-DDSM dataset (three-class classification) and ICIAR2018 dataset (four-class classification).  
the increase of annotated data, eventually approaching convergence as the labeled data ratio reaches 20%.  
This empirical observation is also in line with theoretical studies on learning curve modeling, which describe classifier performance as a function of training data size using inverse power-law relationships [46; 47; 48]. Specifically, performance improvements typically follow a diminishing-returns trend such as \(s(n)=an^{b}+c\), or error reduction \(e(n)\propto n^{-\alpha}\), where \(n\) is the number of labeled samples. These models have been widely used in microarray classification, cancer drug response prediction, and sample size estimation tasks. Our results conform to these theories, confirming that annotation scarcity remains a major limiting factor, while demonstrating the robustness of the proposed DCS-ST method under limited supervision conditions.