# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 1 Introduction  
Medical image segmentation is of importance for clinical diagnosis, pathology research, and the development of treatment plans. A typical situation in the medical image field is the usage of 3D whole-volume data (e.g., CT or MRI data), where the whole volume is a set consisting of tens or hundreds of slices for every patient. With the advent of deep learning, numerous methods based on deep neural networks (DNNs) have been developed and show promising performance for medical image segmentation [1; 2; 3]. The success relies on the existence of an abundance of correctly labeled data. However, this assumption often does not hold for real applications. The label quality heavily depends on the experience and preferences of operators, and less-experienced annotators provide relative noisy labels. Especially for 3D medical images, the data is annotated in 2D and slice-by-slice, resulting in heavy labeling work and inconsistent quality among slices [4]. Many works have proven that low-quality annotations will degrade the performance of models and lead to some misunderstandings for subsequent computer-aided diagnoses. In this study, we aim to develop noise-robust methods for 3D medical image segmentation with noisy labels. Recent methods for learning from noisy labels can be roughly divided as follows. First, a label transition matrix based on pre-defined knowledge is proposed to correct corresponding loss functions [5; 6; 7; 8]. The matrix is used to learn latent noise patterns between noisy and clean annotations. However, they regard categories with clear meanings at states, which is suitable for classification tasks but cannot be directly applied to segmentation tasks. Second, some methods resort to redesigning robust loss functions instead of theconventional loss functions, such as generalized cross-entropy loss [9] and symmetric cross-entropy loss [10]. However, the effectiveness has not been verified for medical image segmentation tasks [11]. Third, many methods are proposed based on the loss re-weighting strategy, which is a very popular way to assign different weights to different samples. They make the relatively clean samples contribute more, while the relatively noisy samples contribute less for the training. The core is designing reliable criteria to estimate and select samples. For example, [PERSON] et al. [12] proposed deploying a meta-learning framework for automatically learning the weights of pixels based on gradient directions, which was an extension of the \"learning to re-weight\" [13] method, from classification tasks to segmentation tasks. However, this method requires extra, trusted subsets of samples for training. [PERSON] et al. [14] introduced a pick-and-learn method with label-quality evaluation and an over-fitting control module. The important weights were assigned for dealing with 2D noisy-labeled image segmentation. [PERSON] et al. [15] proposed a tri-network by extending co-teaching [16], where they maintained three networks simultaneously for sample selection. This method can be regarded as hard-weighting. However, both the pick-and-learn and tri-network methods have been developed for 2D medical images, and cannot be directly applied for resource-intensive 3D medical image segmentation. Presently, the related investigations for 3D biomedical segmentation with low-quality noisy labels have not aroused enough attention. In this paper, we concentrate on the problem of noisy-labeled medical image segmentation, especially for 3D medical volumetric data with tens or hundreds of slices in every whole volume. Specially, we propose a novel hybrid robust-learning architecture to combat noisy labels. First, we propose a novel slice-level label-quality awareness module (LQAM), which is jointly trained with a conventional segmentation module to predict label quality scores for each slice. Inspired by the idea of set-to-set recognition, we regard the whole volume of patients as a set of slices.