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European Journal of Nuclear Medicine and Molecular Imaging
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Volume 46, Issue 13
Pages 2627-2858 (December 2019)
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1. EJNMMI supplement: bringing AI and radiomics to nuclear medicine
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Tác giả
Patrick Veit-Haibach, Irène Buvat.
2. Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications
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Tác giả
Dimitris Visvikis, Catherine Cheze Le Rest.
3. Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis
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Tác giả
Alex Zwanenburg.
4. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
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Tác giả
Martina Sollini, Lidija Antunovic.
5. AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics
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Isabella Castiglioni, Francesca Gallivanone.
6. Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning
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Greg Zaharchuk.
7. Physician centred imaging interpretation is dying out — why should I be a nuclear medicine physician?
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Roland Hustinx.
8. What can artificial intelligence teach us about the molecular mechanisms underlying disease?
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Gary J. R. Cook, Vicky Goh.
9. Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data
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Andreas Holzinger, Benjamin Haibe-Kains.
10. Artificial intelligence and radiomics in nuclear medicine: potentials and challenges
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Tác giả
Cumali Aktolun.
11. Quantitative imaging biomarkers in nuclear medicine: from SUV to image mining studies. Highlights from annals of nuclear medicine 2018
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Tác giả
Martina Sollini, Francesco Bandera.
12. Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI
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Hossein Arabi, Guodong Zeng, Guoyan Zheng.
13. Radiomic features of glucose metabolism enable prediction of outcome in mantle cell lymphoma
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Marius E. Mayerhoefer, Christopher C. Riedl.
14. Integrating manual diagnosis into radiomics for reducing the false positive rate of 18F-FDG PET/CT diagnosis in patients with suspected lung cancer
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Fei Kang, Wei Mu, Jie Gong, Shengjun Wang.
15. PET image denoising using unsupervised deep learning
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Jianan Cui, Kuang Gong, Ning Guo, Chenxi Wu.
16. Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT
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P. J. Brown, J. Zhong, R. Frood, S. Currie.
17. Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics
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Markus Wenzel, Fausto Milletari.
18. Lack of evidence and criteria to evaluate artificial intelligence and radiomics tools to be implemented in clinical settings
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Qian Zhou, Yi-heng Cao, Zhi-hang Chen.
19. Reply to: “Lack of evidence and criteria to evaluate artificial intelligence and radiomics tools to be implemented in clinical settings”
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Martina Sollini, Margarita Kirienko.
20. From molecules to system failure: translational frontiers of multimodal imaging in neurodegenerative diseases
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Thilo van Eimeren, Alexander Drzezga.
21. Connectomics and molecular imaging in neurodegeneration
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Gérard N. Bischof, Michael Ewers.
22. Prospects and challenges of imaging neuroinflammation beyond TSPO in Alzheimer’s disease
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Delphine Boche, Alexander Gerhard.
23. Engineered antibodies: new possibilities for brain PET?
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Tác giả
Dag Sehlin, Stina Syvänen.