# Attention-Based Active Learning Framework for Segmentation of Breast Cancer in Mammograms
## 4 Experimental Results
### Comparative Experiments  
In this subsection, we describe our comparative experiments and present heatmap visualizations, segmentation results and the effectiveness of active learning.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **mDice** & **IoU** & **MAE** \\ \hline \multirow{4}{*}{Breast-A} & UNet & 0.356 & 0.266 & 0.018 \\  & UNet+PFMs & 0.412 & 0.332 & 0.013 \\  & UNet+PFMs+Att & 0.431 & 0.361 & 0.013 \\  & UNet+PFMs+Att+Act & **0.462** & **0.394** & **0.009** \\  & UNet & 0.403 & 0.29 & 0.013 \\  & UNet+PFMs & 0.492 & 0.371 & 0.009 \\  & UNet+PFMs+Att & 0.515 & 0.382 & 0.112 \\  & UNet+PFMs+Att+Act & **0.543** & **0.401** & **0.006** \\ \hline \multirow{4}{*}{Breast-C} & UNet & 0.553 & 0.429 & 0.033 \\  & UNet+PFMs & 0.677 & 0.512 & 0.026 \\ \cline{1-1}  & UNet+PFMs+Att & 0.693 & 0.518 & 0.029 \\ \cline{1-1}  & UNet+PFMs+Att+Act & **0.725** & **0.533** & **0.023** \\ \hline \multirow{4}{*}{Breast-D} & UNet & 0.369 & 0.261 & 0.040 \\ \cline{1-1}  & UNet+PFMs & 0.451 & 0.382 & 0.311 \\ \cline{1-1}  & UNet+PFMs+Att & 0.478 & 0.396 & 0.335 \\ \cline{1-1}  & UNet+PFMs+Att+Act & **0.512** & **0.422** & **0.027** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation experiments with different network structure designs on Breast-A, Breast-B, Breast-C and Breast-D datasets, where PFMs, Att and Act represent multiple promotion feature modules used in the basic segmentation model, the proposed attention-based sampling scheme and the proposed active labeling strategy, respectively.  
Figure 3 shows the generated heatmap of breast cancer segmentation achieved by the proposed framework, the comparative method and the ground truth on four datasets. We used UNet in comparisons of heatmap visualization and segmentation results. The last layer of the network can generate a heatmap for each input breast cancer image, which can be used to generate segmentation results. Comparison of heatmaps shows that the proposed framework could more accurately identify the breast cancer region, and thus obtained a better performance in the segmentation task. Even in the case of a blurred image boundary, the heatmap implies that further segmentation results would maintain high accuracy by focusing on the dominant parts of cancer regions.  
Figure 4 shows the qualitative comparison results of the breast cancer segmentation. Compared with the comparative segmentation model, i.e., UNet, the proposed framework achieved better segmentation results that were similar to the ground-truth results. Moreover, the proposed active learning strategy could refine the distinguished feature information of breast cancer using unlabeled samples, achieving more accurate pixel-level classification results. In addition, the proposed framework generates and refines the boundary region through an effective iterative update strategy, thus achieving global optimization progressively.  
To verify that the active learning strategy can effectively reduce the cost of labeling, we conducted comparative experiments by selecting random sampling and CoreSet [34] as the comparison methods. Figure 5 shows plots of mean Dice for each iteration during the active learning. It is worth noting that the proposed active learning strategy not only had a higher mean Dice value, but also converged in fewer iterations. This proves that the proposed strategy selected more informative samples in each iteration, thus reducing the cost of labeling samples. Without measuring the uncertainty of sample labeling, other comparison methods might suffer from unstable convergence with increasing iterations because they adopt samples without helpful information, or even containing noisy information. Although all methods converged eventually, the compared methods tended to have lower mean Dice values due to the influence of noisy samples.  
Figure 3: Heatmaps of breast cancer segmentation achieved by the proposed framework, the comparative method and the ground truth results. (**A–D**) refer to Breast-A, Breast-B, Breast-C and Breast-D datasets, respectively.