Research map: MAA-Net: Stagewise adaptive anisotropic learning for volumetric segmentation of lymphoma

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  1. Squeeze-and-Excitation Networks · Jie Hu · 2018 · 30406 citations · Cited by this paper
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  23. The 2008 WHO classification of lymphomas: implications for clinical practice and translational research · Elaine S. Jaffe · 2009 · 170 citations · Cited by this paper
  24. Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network. · Paul Blanc‐Durand · 2020 · 148 citations · Cited by this paper
  25. Deep learning for variational multimodality tumor segmentation in PET/CT · Laquan Li · 2019 · 144 citations · Cited by this paper
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  27. 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes · Siqi Liu · 2018 · 139 citations · Cited by this paper
  28. Predictive role of positron emission tomography (PET) in the outcome of lymphoma patients · Pier Luigi Zinzani · 2004 · 100 citations · Cited by this paper
  29. Tumor Segmentation and Feature Extraction from Whole-Body FDG-PET/CT Using Cascaded 2D and 3D Convolutional Neural Networks · Skander Jemaa · 2020 · 78 citations · Cited by this paper
  30. A Mitochondria‐Related Signature in Diffuse Large B‐Cell Lymphoma: Prognosis, Immune and Therapeutic Features · Zhiwei Zhou · 2025 · 22 citations · Cited by this paper
  31. 3D lymphoma segmentation on PET/CT images via multi‐scale information fusion with cross‐attention · H. K. Huang · 2025 · 7 citations · Cited by this paper
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