ULCER
发表于 2025-3-28 17:35:19
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DRAFT
发表于 2025-3-28 19:15:00
,Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival Using a Full-Scalrrent developments of robust deep learning models are hindered by the lack of large multi-centre, multi-modal data with quality annotations. The MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation and outcome prediction challenge creates a platform for comparing segmentation methods of the primar
花束
发表于 2025-3-29 00:26:44
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转换
发表于 2025-3-29 06:12:49
Head and Neck Tumor Segmentation and Outcome PredictionSecond Challenge, HE
LOPE
发表于 2025-3-29 11:13:03
Vincent Andrearczyk,Valentin Oreiller,Adrien Depeu
Tartar
发表于 2025-3-29 13:23:50
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CLOUT
发表于 2025-3-29 18:23:27
CCUT-Net: Pixel-Wise Global Context Channel Attention UT-Net for Head and Neck Tumor Segmentation,bined the global context information and channel information of the image. It not only considered the overall information of the image but also paid attention to the FDG-PET and CT channel information, using the advantages of the two modes to accurately localize the position and segment the boundary
SKIFF
发表于 2025-3-29 19:44:20
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人工制品
发表于 2025-3-30 01:26:29
Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PETvoxel-level threshold approach based on majority voting (AVERAGE), to generate consensus segmentations on the test data by combining the segmentations produced through different trained cross-validation models. We demonstrate that our best performing ensembling approach (256 channels AVERAGE) achiev
神刊
发表于 2025-3-30 05:48:10
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