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Diffuse large B‐cell lymphoma segmentation in PET‐CT images via hybrid learning for feature fusion
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Computer Vision and Pattern Recognition: CVPR 92
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FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0
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Automatic detection and classification of regions of FDG uptake in whole-body PET-CT lymphoma studies
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Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network
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DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images
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A modified Hausdorff distance for object matching
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3D automated lymphoma segmentation in PET images based on cellular automata
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4824615openalex-extracted
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3D lymphoma segmentation on PET/CT images via multi‐scale information fusion with cross‐attention
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Lymphoma Ultrasound Image Segmentation with Self-Attention Mechanism and Stable Learning
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inferred← citeshttps://doi.org/10.1007/s44178-026-00294-5openalex-snapshot
Artificial intelligence-based PET/CT analysis in lymphoma: segmentation, differential diagnosis, and prognostic stratification
inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105529openalex-snapshot
MTR-PET: Multi-temporal resolution PET images for lymphoma segmentation
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PSR-Nets: Deep neural networks with prior shift regularization for PET/CT based automatic, accurate, and calibrated whole-body lymphoma segmentation
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106230openalex-snapshot
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Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging
inferred← citeshttps://doi.org/10.1016/j.cpet.2021.09.006openalex-snapshot
Artificial Intelligence in Lymphoma PET Imaging
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Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: Clinical insights, pitfalls, and observer agreement analyses
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Deep learning for automatic tumor lesions delineation and prognostic assessment in multi-modality PET/CT: A prospective survey
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A dual-validation 3D nnU-Net framework with harmonized preprocessing for robust DLBCL segamentation in PET/CT images
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Deep learning for [18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis
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Artificial Intelligence for Tumor [18F]FDG PET Imaging: Advancements and Future Trends - Part II
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Application of machine learning in the management of lymphoma: Current practice and future prospects
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Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges
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Research Advances of 18F-FDG PET/CT in the Comprehensive Management of Small Cell Lung Cancer
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Multimodal Deep Learning Model on Interim 18F-FDG PET/CT for Predicting Primary Treatment Failure of Diffuse Large B-cell Lymphoma
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The application of machine learning to imaging in hematological oncology: A scoping review
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Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma
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Artificial Intelligence for Risk Stratification in Diffuse Large B-Cell Lymphoma: A Systematic Review of Classification Models and Predictive Performances
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Assessment of Treatment Response in Patients With Diffuse Large B-cell Non-Hodgkin Lymphoma Using the Deauville Criteria: A Case Report and Literature Review
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