Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation

Abstract

The superior performance of CNN on medical image analysis heavily depends on the annotation quality, such as the number of labeled image, the source of image, and the expert experience. The annotation requires great expertise and labour. To deal with the high inter-rater variability, the study of imperfect label has great significance in medical image segmentation tasks. In this paper, we present a novel cascaded robust learning framework for chest X-ray segmentation with imperfect annotation. Our model consists of three independent network, which can effectively learn useful information from the peer networks. The framework includes two stages. In the first stage, we select the clean annotated samples via a model committee setting, the networks are trained by minimizing a segmentation loss using the selected clean samples. In the second stage, we design a joint optimization framework with label correction to gradually correct the wrong annotation and improve the network performance. We conduct experiments on the public chest Xray image datasets collected by Shenzhen Hospital. The results show that our methods could achieve a significant improvement on the accuracy in segmentation tasks compared to the previous methods.

Multimodal Patient-Specific Registration for Breast Imaging Using Biomechanical Modeling with Reference to AI Evaluation of Breast Tumor Change

“…This further paves the way for development of AI detection and evaluation of cancer changes, such as temporal subtraction where the changes can be detected at different time intervals. Previous research also showed the superior performance of AI algorithms [ 2 , 3 , 4 ]. The registration of PET and MRI remains challenging for three reasons: (1) the patient position is different during PET and MR scanning.…”

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

“…They trained the segmentation model on examples with clean annotations. For chest X-ray segmentation with imperfect labels, Xue et al [298] adopted a cascade strategy consisting of two stages: a sample selection stage, which selects the clean annotated examples as the co-teaching paradigm, and a label correction and model learning stage, which learns the segmentation model from both the corrected labels and original labels. To segment skin lesions from noisy annotations, Mirikharaji et al [299] adopted a spatially adaptive reweighting approach to emphasize the learning from clean labels and reduce the side effect of noisy pixel-level annotations.…”

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