Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients

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 ext...

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Veröffentlicht in:Australasian physical & engineering sciences in medicine 2024-09, Vol.47 (3), p.833-849
Hauptverfasser: Yousefirizi, Fereshteh, Shiri, Isaac, O, Joo Hyun, Bloise, Ingrid, Martineau, Patrick, Wilson, Don, Bénard, François, Sehn, Laurie H., Savage, Kerry J., Zaidi, Habib, Uribe, Carlos F., Rahmim, Arman
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Sprache:eng
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