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

04/05/2021
by   Cheng Xue, et al.
10

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 X-ray 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.

READ FULL TEXT

page 5

page 6

research
05/10/2022

Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training

Deep neural networks have achieved remarkable success in a wide variety ...
research
03/03/2022

Semantic-guided Image Virtual Attribute Learning for Noisy Multi-label Chest X-ray Classification

Deep learning methods have shown outstanding classification accuracy in ...
research
07/27/2019

Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation

Deep learning methods have achieved promising performance in many areas,...
research
04/03/2022

Exemplar Learning for Medical Image Segmentation

Medical image annotation typically requires expert knowledge and hence i...
research
09/22/2020

Learning Image Labels On-the-fly for Training Robust Classification Models

Current deep learning paradigms largely benefit from the tremendous amou...
research
01/23/2019

Robust Learning at Noisy Labeled Medical Images:Applied to Skin Lesion Classification

Deep neural networks (DNNs) have achieved great success in a wide variet...
research
08/07/2020

Few Shot Learning Framework to Reduce Inter-observer Variability in Medical Images

Most computer aided pathology detection systems rely on large volumes of...

Please sign up or login with your details

Forgot password? Click here to reset