Article ; Online: Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients.
Physical and engineering sciences in medicine
2024
Abstract: Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in ...
Abstract | Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in PET scans. However, a major hurdle is the extensive amount of supervised and annotated data necessary for training. To overcome this limitation, this study explores semi-supervised approaches utilizing unlabeled data, specifically focusing on PET images of diffuse large B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL) obtained from two centers. We considered 2-[ |
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Language | English |
Publishing date | 2024-03-21 |
Publishing country | Switzerland |
Document type | Journal Article |
ISSN | 2662-4737 |
ISSN (online) | 2662-4737 |
DOI | 10.1007/s13246-024-01408-x |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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