# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 3 Methods
### Label-Quality Awareness Module  
A label-quality awareness module (LQAM) is proposed to automatically learn the quality of labels among slices, thus reducing the impact of noisy labels with the re-weighting strategy. Considering that the slices that belong to the whole volume are annotated slice-by-slice and in 2D by less-experienced operators, the label quality of slices may be noisy and inconsistent. We regard the whole-volume data as the set of slices, and the quality scores of slices as another set.  
In this section, we design our label-quality awareness module to estimate label quality of slices. The set \(S=\{I_{1},I_{2},\ldots,I_{N}\}\) is used to denote the set of slices with different label qualities, which can be compared with each other. We use \(I_{i}\) to denote the information of the \(i\)-th slice and use \(N\) to represent the number of slices belonging to one volume. Let \(Q(S)\) denote the set of quality scores belonging to the whole -volume, satisfying:  
\[Q(S)=F(I_{1},I_{2},\ldots,I_{N}), \tag{1}\]  
where \(F(\cdot)\) is the quality-awareness function. The challenge is to find an optimized \(F(\cdot)\), which aggregates features from the whole volumetric set to obtain different quality scores. As neural networks could approximate various functions, we obtain the quality scores via neural network learning [38; 30]. The notion is that images with higher-quality labeled information are easier to recognize, while lower-quality labeled information are hard to fit. This is related to the memorization of neural networks, and has been widely used for the recognition of noisy labels in existing natural image classification tasks with noisy labels [39]. We also use this trick to estimate the label quality of the slice set. More specifically, the slices with noisy labels have relatively higher losses, while the slices with well-annotated labels have relatively smaller losses during the training process. Therefore, with the help of losses coming from the main segmentation network, the LQAM could assess the relative quality of every slice.  
We construct our label-quality awareness module using deep neural networks, which are trained together with a main segmentation network. We focus on learning with noisy labels where the images are intact and the labels are noisy. As for the input information to the LQAM, we use the set \(S=\{I_{1},I_{2},\ldots,I_{N}\}\). In order to deal with noisy labels, the concatenations of image, foreground, and background are used as the whole input information \(I_{i}\) of the \(i\)-th slice. We use these three components to contain enough information about noisy labels, which is used for subsequent label-quality awareness. Moreover, the LQAM module is jointly trained with the main segmentation. We only use this kind of concatenation as the input to keep the number of parameters constant, regardless of the number of organs. This construction is very helpful for satisfying the memory limitations of GPUs [40], and is appliable for resource-intensive volumetric segmentation. After obtaining the label quality scores of slices, the segmentation loss could be adjusted to combat noisy labels. With the main segmentation network and the label-quality awareness network, we construct our hybrid noise-robust architecture in Section 3.4.