Primary tumour type impacts 3D U-Net for [18F]FDG PET lesion segmentation performance in cases of lung cancer, melanoma, and lymphoma

Primary tumour type impacts 3D U-Net for [¹⁸F]FDG PET lesion segmentation performance in cases of lung cancer, melanoma, and lymphoma

Research output: Contribution to journal › Meeting Abstract › peer-review

Original language	English
Pages (from-to)	S271-S272
Number of pages	2
Journal	European Journal of Nuclear Medicine and Molecular Imaging
Volume	50
Issue number	SUPPL 1
Publication status	Published - Sept 2023

This output contributes to the following UN Sustainable Development Goals (SDGs)

@article{d1efae66f6fc46278f613b38a7c2a1a3,

title = "Primary tumour type impacts 3D U-Net for [18F]FDG PET lesion segmentation performance in cases of lung cancer, melanoma, and lymphoma",

author = "M. Sobral and M. Oliveira and C. Constantino and F. Oliveira and N. Matela and Costa, \{D. C.\}",

year = "2023",

month = sep,

language = "English",

volume = "50",

pages = "S271--S272",

journal = "European Journal of Nuclear Medicine and Molecular Imaging",

issn = "1619-7070",

publisher = "Springer",

number = "SUPPL 1",

}

Research output: Contribution to journal › Meeting Abstract › peer-review

TY - JOUR

T1 - Primary tumour type impacts 3D U-Net for [18F]FDG PET lesion segmentation performance in cases of lung cancer, melanoma, and lymphoma

AU - Sobral, M.

AU - Oliveira, M.

AU - Constantino, C.

AU - Oliveira, F.

AU - Matela, N.

AU - Costa, D. C.

PY - 2023/9

Y1 - 2023/9

M3 - Meeting Abstract

SN - 1619-7070

VL - 50

SP - S271-S272

JO - European Journal of Nuclear Medicine and Molecular Imaging

JF - European Journal of Nuclear Medicine and Molecular Imaging

IS - SUPPL 1

ER -