# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 4 Experiments and Results
### Comparisons on Liver Segmentation Dataset  
We conduct experiments on a liver segmentation benchmark to demonstrate the effectiveness of our hybrid robust architecture. The dataset has 3D CT images. We train our network on the noisy-labeled data under different noise settings and test the network with clean-labeled data. Specifically, we set two noise levels (noise level 1 of 1-8 pixels and noise level 2 of 5-18 pixels) and three noise rates (noise rate = 25%, 50%, and 75%). Following the generation process of noisy labels [14; 15], we obtain the data with simulated noisy labels.  
The comparing baselines include: (1) Plain [35]: the conventional training based on a 3D segmentation model with noisy labels. We use 3D U-net with the additions of residual blocks and 3D SE blocks as the backbone. (2) Pick-and-learn [14]: a representative method for addressing 2D medical image segmentation with noisy labels. We regard every slice as a 2D datum and further adopt the pick-and-learn strategy for the corresponding robust learning. (3) Disagreement [33]: a noise-robust method from the perspective of sample selection, which selects pixels from the disagreement area to obtain informative samples. (4) Area-aware method proposed by [44]: the area aware-factor is calculated as the ratio between the area of the foreground and the area of the background, and is then introduced by a simple multiplication strategy. (5) INT [45]: this method aims to distill effective supervision information from image-level data to develop a noise-tolerance algorithm.  
The experimental results on the liver segmentation dataset with simulated noisy labels are illustrated in Table 1. On the clean-annotated dataset, we have the performance upper as the dice value of 88.55. However, the liver segmentation performance for the Plain baseline decreases sharply when adding different noise rates (from no noise to 25%, 50%, and 75%) and noise levels (from noise level 1 of 1-8 pixels to noise level 2 of 5-18 pixels). Comparing with other baselines, we could observe that our proposed hybrid architecture has a consistent performance improvement. Even in the hardest case, with noise level 2 and a noise rate of 75%, our proposed method shows its robustness to noisy labels.  
Comparing with pick-and-learn, our method achieves performance gains, which demonstrates that adopting a 3D network as a backbone is more appropriate because of the implicit complementary information among slices. Comparing with the disagreement method, our superior performance shows that our hybrid architecture could mine more effective information, while disagreement only selects samples from a disagreement area, resulting few samples during training. For image-level noise-robust learning, we compare an INT (image-level noise-tolerance) method with our method. The results show the improvement, which verifies the effectiveness of our method from the perspective of slice-level noise tolerance. We further observe the results of our method and the area-aware strategy, which also verifies that our slice-level label-quality awareness and regularization loss show better performance advantages.