# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 2 Related Works  
We review some related works for developing noise-robust methods. The representative methods can be roughly divided as follows. A label transition matrix based on pre-defined knowledge is proposed to correct the corresponding loss function. The matrix is used to learn latent noise patterns between noisy and clean annotations [17]. The key is how to accurately estimate the label transition matrix. For example, [PERSON] et al. added a linear noise layer at the end of a backbone convolutional neural network to implicitly estimate the matrix [18]. [PERSON] et al. estimated a transition matrix by forward and backward loss-correction, which depended on strong assumptions to stack the maximum of each class [5]. [PERSON] et al. proposed gold loss-correction via mean prediction, which relied on extra, trusted subsets of data to accurately estimate the corrected matrix [6]. [PERSON] et al. adapted a meta-learning method to directly optimize a noise transition matrix with the help of a large noisy dataset and a small trusted subset [7]. [PERSON] et al. proposed the introduction of prior knowledge for estimating a noise transition matrix [19]. As for medical applications, [PERSON] et al. followed the line of a noise-adaptation layer to represent the transition matrix and checked its applicability for breast microcalcification classification [8]. However, the above methods regard categories with clear meanings at states, which is suitable for classification tasks but cannot be directly applied to segmentation tasks. Some methods focus on re-designing a robust loss function to combat noisy labels, which means they design a new robust loss function as the alternative to the conventional loss function in the presence of noisy labels. For example, [PERSON] and [PERSON] et al. proposed generalized cross-entropy loss [9], which combined the advantages of categorical cross-entropy and mean absolute error [20]. [PERSON] et al. proposed a popular symmetric cross-entropy loss, which was the combination of cross-entropy and reverse cross-entropy [10]. [PERSON] et al. leveraged gradient clipping for designing new loss functions [21]. However, these robust loss functions are developed based on their specific constraints, and the effectiveness may be reduced when encountering relatively complex medical segmentation data [11]. Some regularization methods have also been proposed to reduce the overfitting effect based on explicit or implicit forms. The former applies an explicit form to modify the training loss, such as early-learning regularization [22] or trace regularization [23]. The latter means they have the similar effect of regularization without the explicit form [24; 25; 26; 27]. However, this kind of method should be designed with specific characteristics or it should introduce sensitive model-dependent hyperparameters. Some methods are proposed based on the idea of loss re-weighting. Here, they make the relatively clean samples contribute more, while the relatively noisy samples contribute less for the training. The core of this method is how to design the accurate criterion to estimate and select samples. For example, [PERSON] et al. trained a mentor network to guide a student network by assigning weights to samples [28]. [PERSON] et al. calculated sample weights by modeling losses per-sample with a mixture model [29]. [PERSON] et al. used multiple class prototypes to assign attention weights to data samples [30]. [PERSON] et al. designed an additional network to decide whether a label was noisy or not and produced the weight of each sample to reduce the influence of noisy labels [31]. [PERSON] et al. proposed re-weighting samples based on their gradient directions and a meta-learning framework [13]. Under this investigation, [PERSON] et al. proposed an explicit mapping method for sample weighting based on a meta-weight net [32].