| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics
|
Castiglioni, Isabella
|
|
|
46 |
13 |
p. 2673-2699
|
article
|
| 2 |
Artificial intelligence and radiomics in nuclear medicine: potentials and challenges
|
Aktolun, Cumali
|
|
|
46 |
13 |
p. 2731-2736
|
article
|
| 3 |
Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications
|
Visvikis, Dimitris
|
|
|
46 |
13 |
p. 2630-2637
|
article
|
| 4 |
Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics
|
Wenzel, Markus
|
|
|
46 |
13 |
p. 2800-2811
|
article
|
| 5 |
Connectomics and molecular imaging in neurodegeneration
|
Bischof, Gérard N.
|
|
|
46 |
13 |
p. 2819-2830
|
article
|
| 6 |
EJNMMI supplement: bringing AI and radiomics to nuclear medicine
|
Veit-Haibach, Patrick
|
|
|
46 |
13 |
p. 2627-2629
|
article
|
| 7 |
Engineered antibodies: new possibilities for brain PET?
|
Sehlin, Dag
|
|
|
46 |
13 |
p. 2848-2858
|
article
|
| 8 |
From molecules to system failure: translational frontiers of multimodal imaging in neurodegenerative diseases
|
Eimeren, Thilo van
|
|
|
46 |
13 |
p. 2816-2818
|
article
|
| 9 |
Integrating manual diagnosis into radiomics for reducing the false positive rate of 18F-FDG PET/CT diagnosis in patients with suspected lung cancer
|
Kang, Fei
|
|
|
46 |
13 |
p. 2770-2779
|
article
|
| 10 |
Lack of evidence and criteria to evaluate artificial intelligence and radiomics tools to be implemented in clinical settings
|
Zhou, Qian
|
|
|
46 |
13 |
p. 2812-2813
|
article
|
| 11 |
Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning
|
Zaharchuk, Greg
|
|
|
46 |
13 |
p. 2700-2707
|
article
|
| 12 |
Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI
|
Arabi, Hossein
|
|
|
46 |
13 |
p. 2746-2759
|
article
|
| 13 |
PET image denoising using unsupervised deep learning
|
Cui, Jianan
|
|
|
46 |
13 |
p. 2780-2789
|
article
|
| 14 |
Physician centred imaging interpretation is dying out — why should I be a nuclear medicine physician?
|
Hustinx, Roland
|
|
|
46 |
13 |
p. 2708-2714
|
article
|
| 15 |
Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT
|
Brown, P. J.
|
|
|
46 |
13 |
p. 2790-2799
|
article
|
| 16 |
Prospects and challenges of imaging neuroinflammation beyond TSPO in Alzheimer’s disease
|
Boche, Delphine
|
|
|
46 |
13 |
p. 2831-2847
|
article
|
| 17 |
Quantitative imaging biomarkers in nuclear medicine: from SUV to image mining studies. Highlights from annals of nuclear medicine 2018
|
Sollini, Martina
|
|
|
46 |
13 |
p. 2737-2745
|
article
|
| 18 |
Radiomic features of glucose metabolism enable prediction of outcome in mantle cell lymphoma
|
Mayerhoefer, Marius E.
|
|
|
46 |
13 |
p. 2760-2769
|
article
|
| 19 |
Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis
|
Zwanenburg, Alex
|
|
|
46 |
13 |
p. 2638-2655
|
article
|
| 20 |
Reply to: “Lack of evidence and criteria to evaluate artificial intelligence and radiomics tools to be implemented in clinical settings”
|
Sollini, Martina
|
|
|
46 |
13 |
p. 2814-2815
|
article
|
| 21 |
Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
|
Sollini, Martina
|
|
|
46 |
13 |
p. 2656-2672
|
article
|
| 22 |
What can artificial intelligence teach us about the molecular mechanisms underlying disease?
|
Cook, Gary J. R.
|
|
|
46 |
13 |
p. 2715-2721
|
article
|
| 23 |
Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data
|
Holzinger, Andreas
|
|
|
46 |
13 |
p. 2722-2730
|
article
|
Journal description