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Recently, deep learning enabled the accurate segmentation of various diseases in medical imaging.
“Pet/ct today and tomorrow,”
David W Townsend et al., · 2004
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“Application of pet and pet/ct imaging for cancer screening,”
Yen-Kung Chen et al., · 2004
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“An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision,”
Y. Boykov and V. Kolmogorov, · 2004
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“Geos: Geodesic image segmentation,”
Antonio Criminisi et al., · 2008
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“Ilastik: Interactive learning and segmentation toolkit,”
Christoph Sommer et al., · 2011
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“3d slicer as an image computing platform for the quantitative imaging network,”
Andriy Fedorov et al., · 2012
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“Fdg pet and pet/ct,”
Berud J Krause et al., · 2013
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“Adam: A method for stochastic optimization,”
Diederik P Kingma and Jimmy Ba, · 2014
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“Imagenet large scale visual recognition challenge,”
Olga Russakovsky et al., · 2015
Cited alongside, same era.
“U-net: Convolutional networks for biomedical image segmentation,”
Olaf Ronneberger et al., · 2015
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“Deepigeos: a deep interactive geodesic framework for medical image segmentation,”
Guotai Wang et al., · 2018
Cited alongside, same era.
“Interactive medical image segmentation using deep learning with image-specific fine tuning,”
Guotai Wang et al., · 2018
Cited alongside, same era.
“The upsurge of deep learning for computer vision applications,”
Priyanka Patel and Amit Thakkar, · 2020
Cited alongside, same era.
“An image is worth 16x16 words: Transformers for image recognition at scale,”
“Assessing the difficulty of annotating medical data in crowdworking with help of experiments,”
Anne Rother et al., · 2021
Later among the works it cites.
“Mideepseg: Minimally interactive segmentation of unseen objects from medical images using deep learning,”
Xiangde Luo et al., · 2021
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“Going to extremes: weakly supervised medical image segmentation,”
Holger R Roth et al., · 2021
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“Deepscribble: interactive pathology image segmentation using deep neural networks with scribbles,”
Sungduk Cho et al., · 2021
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“Wdtiseg: One-stage interactive segmentation for breast ultrasound image using weighted distance transform and shape-aware compound loss,”
Xiaokang Li et al., · 2021
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Alexey Dosovitskiy et al., · 2020
Cited alongside, same era.
“Uncertainty-guided efficient interactive refinement of fetal brain segmentation from stacks of mri slices,”
Guotai Wang et al., · 2020
Cited alongside, same era.
“A large-scale ct and pet/ct dataset for lung cancer diagnosis,” 2020
Ping Li et al., · 2020
Cited alongside, same era.
Sergios Gatidis, Tobias Hepp, Marcel Früh, Christian La Fougère, Konstantin Nikolaou, Christina Pfannenberg, Bernhard Schölkopf, Thomas Küstner, Clemens Cyran, and Daniel Rubin, · 2022
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“Monai label: A framework for ai-assisted interactive labeling of 3d medical images,” 2022
Andres Diaz-Pinto et al., · 2022
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