ArticlePhysical and engineering sciences in medicine2024
Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients.
Article in Physical and engineering sciences in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Comparison of Automatic Segmentation and Preprocessing Approaches for Dynamic Total-Body 3D Pet Images with Different Pet Tracers.Journal of imaging informatics in medicine · 2026Article
- Segmentation of Dynamic Total-Body [International journal of biomedical imaging · 2023Article
- Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges.Digital healthReview
Corrections and comments
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Authors and funding
12 authors.
Funding
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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Registered trials
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