Abstract：
PURPOSE:Recently, brain tumor segmentation has made important progress. However, the quality of manual labels plays an important role in the performance, while in practice, it could vary greatly and in turn could substantially mislead the learning process and decrease the accuracy. We need to design a mechanism to combine label correction and sample reweighting to improve the effectiveness of brain tumor segmentation. METHODS:We propose a novel sample reweighting and label refinement method, and a novel three-dimensional (3D) generative adversarial network (GAN) is introduced to combine these two models into an united framework. RESULTS:Extensive experiments on the BraTS19 dataset have demonstrated that our approach obtains competitive results when compared with other state-of-the-art approaches when handling the false labels in brain tumor segmentation. CONCLUSIONS:The 3D GAN-based approach is an effective approach to handle false label masks by simultaneously applying label correction and sample reweighting. Our method is robust to variations in tumor shape and background clutter.
journal_name
Med Physjournal_title
Medical physicsauthors
Cheng G,Ji H,He Ldoi
10.1002/mp.14480subject
Has Abstractpub_date
2020-09-24 00:00:00eissn
0094-2405issn
2473-4209pub_type
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