# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 4 Experiments and Results
### Ablation Study and Visualization  
We also show the ablation study in Figure 2. We use \"_Plain_\" to denote the basic baseline, where the model is trained without any noise-robust strategy. We use \"\(LQAM\)\" to represent our hybrid architecture with only slice-level label-quality awareness. We use \"\(LQAM+L_{R}\)\" to denote our proposed hybrid architecture with \(LQAM\) and regularization loss \(L_{R}\). The experiments are conducted on a liver segmentation dataset with different noise rates and noise levels. Comparing \(LQAM\) and \(Plain\), we can observe that adding a label-quality awareness module is necessary for developing noise-robust methods. The comparisons between \(LQAM\) and \(LQAM+L_{R}\) have shown the effectiveness of introducing shape-awareness regularization loss. This may be because the shape-awareness loss will benefit from learning some boundary information. Together with the re-weighted framework, the regularization loss helps to further improve the contribution of relatively clean samples. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Noise Rates** & **Method** & **Liver** & **Right Kidney** & **Left Kidney** & **Spleen** & **Average** \\ \hline No noise & Plain [35] & 84.20 & 75.13 & 64.93 & 73.66 & 74.48 \\ \hline \multirow{4}{*}{25\%} & Plain [35] & 79.36 & 55.09 & 42.88 & 52.51 & 57.46 \\  & Pick-and-learn [14] & 80.31 & 45.79 & 38.33 & 38.44 & 50.72 \\  & Disagreement [33] & 73.46 & 49.79 & 44.66 & 52.09 & 55.00 \\  & INT [45] & 78.93 & 56.63 & 45.66 & 59.14 & 60.09 \\  & Area-aware [44] & 75.05 & 52.83 & 54.17 & 51.11 & 58.29 \\  & Ours & 78.00 & 60.72 & 49.62 & 60.95 & 62.32 \\ \hline \multirow{4}{*}{50\%} & Plain [35] & 76.86 & 52.43 & 42.75 & 54.40 & 56.61 \\  & Pick-and-learn [14] & 70.36 & 48.87 & 41.26 & 48.55 & 52.26 \\  & Disagreement [33] & 71.37 & 49.87 & 41.26 & 49.55 & 53.01 \\  & INT [45] & 79.10 & 55.10 & 46.97 & 56.29 & 59.37 \\  & Area-aware [44] & 75.47 & 51.80 & 46.66 & 54.19 & 57.03 \\  & Ours & 80.27 & 54.07 & 47.54 & 60.49 & 61.60 \\ \hline \multirow{4}{*}{75\%} & Plain [35] & 75.18 & 56.29 & 41.75 & 52.10 & 56.33 \\  & Pick-and-learn [14] & 70.79 & 48.13 & 36.83 & 44.22 & 49.99 \\ \cline{1-1}  & Disagreement [33] & 72.99 & 48.49 & 40.41 & 46.42 & 52.08 \\ \cline{1-1}  & INT [45] & 76.66 & 50.69 & 47.25 & 57.63 & 58.06 \\ \cline{1-1}  & Area-aware [44] & 76.47 & 48.99 & 45.12 & 57.20 & 56.94 \\ \cline{1-1}  & Ours & 78.62 & 52.08 & 51.84 & 59.62 & 60.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results (dice %) for multi-organ segmentation on abdomen MRI-T2 SPIR. We report the performances of the liver, right kidney, left kidney, and spleen, as well as the average values. Figure 2: The results of ablation study. The experiments are conducted on the liver segmentation dataset under different noise settings. We visualize some slices to show the effectiveness of our hybrid noise-robust architecture in Figure 3. For the liver segmentation dataset, we randomly select slices with noise levels of 5-18 pixels and a noise rate of 25%. For multi-organ segmentation, we show the slices with a noise level of 1-5 pixels and a noise rate of 25%. We also show some bad examples in Figure 4, where the slices are selected from multi-organ segmentation on the abdomen MRI-T2 SPIR dataset. Note that the 3D whole-volume data is a set consisting of tens or hundreds of slices for every patient. The size variances of organs among different slices are very obvious, and sometimes the organs present a relatively small size on some slices. The bad case, on the left, is the example. The organ size is relatively small in this slice, and the performance is poor. We should investigate the segmentation of small organs to further improve the performance of learning with noisy labels, which is the future work. When we observe the bad case on the right, the results show that there are some overlaps among different organs based on our method.