Characterizing Label Errors: Confident Learning for Noisy-Labeled Image Segmentation

摘要

Convolutional neural networks (CNNs) have achieved remarkable performance in image processing for its mighty capability to fit huge amount of data. However, if the training data are corrupted by noisy labels, the resulting performance might be deteriorated. In the domain of medical image analysis, this dilemma becomes extremely severe. This is because the medical image annotation always requires medical expertise and clinical experience, which would inevitably introduce subjectivity. In this paper, we design a novel algorithm based on the teacher-student architecture for noisy-labeled medical image segmentation. Creatively, We introduce confident learning (CL) method to identify the corrupted labels and endow CNN an anti-interference ability to the noises. Specifically, the CL technique is introduced to the teacher model to characterize the suspected wrong-labeled pixels. Since the noise identification maps are a little away from sufficient precision, the spatial label smoothing regularization technique is utilized to generate soft-corrected masks for training the student model. Since our method identifies and revises the noisy labels of the training data in a pixel-level rather than simply assigns lower weights to the noisy masks, it outperforms the state-of-the-art method in the noisy-labeled image segmentation task on the JSRT dataset, especially when the training data are severely corrupted by noises.