| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
Addressing signal alterations induced in CT images by deep learning processing: A preliminary phantom study
|
Doria, Sandra
|
|
|
83 |
C |
p. 88-100
|
artikel
|
| 2 |
A deep learning classifier for digital breast tomosynthesis
|
Ricciardi, R.
|
|
|
83 |
C |
p. 184-193
|
artikel
|
| 3 |
AI applications to medical images: From machine learning to deep learning
|
Castiglioni, Isabella
|
|
|
83 |
C |
p. 9-24
|
artikel
|
| 4 |
Aims & Scope & Editorial Board
|
|
|
|
83 |
C |
p. i
|
artikel
|
| 5 |
Artificial intelligence and machine learning for medical imaging: A technology review
|
Barragán-Montero, Ana
|
|
|
83 |
C |
p. 242-256
|
artikel
|
| 6 |
Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy
|
Avanzo, Michele
|
|
|
83 |
C |
p. 221-241
|
artikel
|
| 7 |
Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization
|
Papadimitroulas, Panagiotis
|
|
|
83 |
C |
p. 108-121
|
artikel
|
| 8 |
Automatic deep learning-based pleural effusion classification in lung ultrasound images for respiratory pathology diagnosis
|
Tsai, Chung-Han
|
|
|
83 |
C |
p. 38-45
|
artikel
|
| 9 |
Automating chest radiograph imaging quality control
|
Nousiainen, Katri
|
|
|
83 |
C |
p. 138-145
|
artikel
|
| 10 |
Basic of machine learning and deep learning in imaging for medical physicists
|
Manco, Luigi
|
|
|
83 |
C |
p. 194-205
|
artikel
|
| 11 |
Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features
|
Astaraki, Mehdi
|
|
|
83 |
C |
p. 146-153
|
artikel
|
| 12 |
Breast glandularity and mean glandular dose assessment using a deep learning framework: Virtual patients study
|
Massera, Rodrigo T.
|
|
|
83 |
C |
p. 264-277
|
artikel
|
| 13 |
Conditional generative adversarial networks to generate pseudo low monoenergetic CT image from a single-tube voltage CT scanner
|
Funama, Yoshinori
|
|
|
83 |
C |
p. 46-51
|
artikel
|
| 14 |
Current applications of deep-learning in neuro-oncological MRI
|
Zegers, C.M.L.
|
|
|
83 |
C |
p. 161-173
|
artikel
|
| 15 |
Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools
|
Diaz, Oliver
|
|
|
83 |
C |
p. 25-37
|
artikel
|
| 16 |
Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance
|
Barragán-Montero, Ana M.
|
|
|
83 |
C |
p. 52-63
|
artikel
|
| 17 |
Enterprise imaging and big data: A review from a medical physics perspective
|
McCarthy, Nicholas
|
|
|
83 |
C |
p. 206-220
|
artikel
|
| 18 |
Expanding the medical physicist curricular and professional programme to include Artificial Intelligence
|
Zanca, F.
|
|
|
83 |
C |
p. 174-183
|
artikel
|
| 19 |
Focus issue: Artificial intelligence in medical physics
|
Zanca, F.
|
|
|
83 |
C |
p. 287-291
|
artikel
|
| 20 |
Machine learning in Magnetic Resonance Imaging: Image reconstruction
|
Montalt-Tordera, Javier
|
|
|
83 |
C |
p. 79-87
|
artikel
|
| 21 |
Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation
|
Rizk, B.
|
|
|
83 |
C |
p. 64-71
|
artikel
|
| 22 |
Performance of an artificial intelligence tool with real-time clinical workflow integration – Detection of intracranial hemorrhage and pulmonary embolism
|
Buls, Nico
|
|
|
83 |
C |
p. 154-160
|
artikel
|
| 23 |
Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice
|
Bosmans, Hilde
|
|
|
83 |
C |
p. 257-263
|
artikel
|
| 24 |
Radiation therapy dose prediction for left-sided breast cancers using two-dimensional and three-dimensional deep learning models
|
Hedden, Natasha
|
|
|
83 |
C |
p. 101-107
|
artikel
|
| 25 |
Radiomics classifier to quantify automatic segmentation quality of cardiac sub-structures for radiotherapy treatment planning
|
Maffei, Nicola
|
|
|
83 |
C |
p. 278-286
|
artikel
|
| 26 |
Requirements and reliability of AI in the medical context
|
Balagurunathan, Yoganand
|
|
|
83 |
C |
p. 72-78
|
artikel
|
| 27 |
The EU medical device regulation: Implications for artificial intelligence-based medical device software in medical physics
|
Beckers, R.
|
|
|
83 |
C |
p. 1-8
|
artikel
|
| 28 |
The promise of artificial intelligence and deep learning in PET and SPECT imaging
|
Arabi, Hossein
|
|
|
83 |
C |
p. 122-137
|
artikel
|
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