Semi-supervised tooth instance segmentation
Tooth segmentation is getting popular with the development of 3D computer vision technology. Current tooth segmentation models rely on large annotations of data which requires great effort from experts and increases the computation cost for training. In this report we proposed to implement mea...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176834 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Tooth segmentation is getting popular with the development of 3D computer vision
technology. Current tooth segmentation models rely on large annotations of data which
requires great effort from experts and increases the computation cost for training. In this
report we proposed to implement mean teacher, a semi-supervised learning framework to
train the tooth instance segmentation model. Our experiment results shows that our
network can achieve comparable performance with fully supervised network but requires
far less data annotation and computation cost. |
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