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yulequan / UA-MT

code for MICCAI 2019 paper 'Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation'.

Home Page:https://arxiv.org/abs/1907.07034

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Guidance about pipeline

Abbas009 opened this issue · comments

commented

Thank you for sharing your code. I have learned a lot from this project. I have some queries please answer them when available.

  1. You make the H5 file which contains "image" and "label" although is a label or unlabeled data. Although, you didn't use it later for unlabeled data. So what if I have only "image" for data and do not "label" for it. Datagenrator function will give an error and can you tell me how to solve it.
  2. I am trying on some other data taken from the hospital, based on your knowledge can you please guide what ratio of the label and unlabeled data can be used to get optimal performance . And can we get a dice score more than fully supervised learning with more unlabeled data or it will decrease the performance?
commented

Hello, may I ask how to convert the image data without labels into H5 files

commented

Hello, I have the same question over the data process, the code doesn't have the part of unlabeled data processing, so how can we train the unlabeled data in the teacher model?