Semi-supervised Left Atrium Segmentation with Mutual Consistency Training
By Yicheng Wu, Minfeng Xu, ZongYuan Ge, Jianfei Cai, Lei Zhang. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021
348 citations recorded by the source corpus.
Semi-supervised learning has attracted great attention in the field of machine learning, especially for medical image segmentation tasks, since it alleviates the heavy burden of collecting abundant densely annotated data for training. However, most of existing methods underestimate the importance of challenging regions (e.g. small branches or blurred edges) during training. We believe that these unlabeled regions may contain more crucial information to minimize the uncertainty prediction for the model and should be emphasized in the training process. Therefore, in this paper, we propose a novel Mutual Consistency Network (MC-Net) for semi-supervised left atrium segmentation from 3D MR images. Particularly, our MC-Net consists of one encoder and two slightly different decoders, and the prediction discrepancies of two decoders are transformed as an unsupervised loss by our designed cycled pseudo label scheme to encourage mutual consistency. Such mutual consistency encourages the two decoders to have consistent and low-entropy predictions and enables the model to gradually capture generalized features from these unlabeled challenging regions. We evaluate our MC-Net on the public Left Atrium (LA) database and it obtains impressive performance gains by exploiting the unlabeled data effectively. Our MC-Net outperforms six recent semi-supervised methods for left atrium segmentation, and sets the new state-of-the-art performance on the LA database.
Subfields: Cardiology and Cardiovascular Medicine, Epidemiology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/9fee2e8cdc0bdd0f00126e155aaeab4a0ccf0b47
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation (2023, neutral): “LA dataset We compare our framework on LA dataset with various competitors: UA-MT [41], SASSNet [12], DTC [16], URPC [17], MC-Net [36] and SS-Net [35].”
- Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, background): “However, the hyperparameter needs to be set carefully and different samples may have different T , so [91] propose an adaptive sharpening which can adjust T adaptively for each sample according to its uncertainty predicted by the model.”