A Segmentation Network with a Class-Agnostic Loss Function for Training on Incomplete Data
The use of deep learning methods is increasing in medical image analysis, e.g., segmentation of organs in medical images. Deep learning methods are highly dependent on a large amount of training data, a common obstacle for medical image analysis. This master thesis proposes a class-agnostic loss fun...
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| Format: | Others |
| Language: | English |
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KTH, Medicinteknik och hälsosystem
2020
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276935 |