Triple-view feature learning for medical image segmentation
Deep learning models, e.g. supervised Encoder-Decoder style networks, exhibit promising performance in medical image segmentation, but come with a high labelling cost. We propose TriSegNet, a semi-supervised semantic segmentation framework. It uses triple-view feature learning on a limited amount of...
| Автори: | , |
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| Формат: | Conference item |
| Мова: | English |
| Опубліковано: |
Springer
2022
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