| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity
|
Liu, Manxia
|
|
2019
|
95 |
C |
p. 104-117
|
artikel
|
| 2 |
A methodology for customizing clinical tests for esophageal cancer based on patient preferences
|
Roy, Asis
|
|
2019
|
95 |
C |
p. 16-26
|
artikel
|
| 3 |
A survey on computer-assisted Parkinson's Disease diagnosis
|
Pereira, Clayton R.
|
|
2019
|
95 |
C |
p. 48-63
|
artikel
|
| 4 |
Computational methods for Gene Regulatory Networks reconstruction and analysis: A review
|
Delgado, Fernando M.
|
|
2019
|
95 |
C |
p. 133-145
|
artikel
|
| 5 |
Computational normalization of H&E-stained histological images: Progress, challenges and future potential
|
Azevedo Tosta, Thaína A.
|
|
2019
|
95 |
C |
p. 118-132
|
artikel
|
| 6 |
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
|
Bernal, Jose
|
|
2019
|
95 |
C |
p. 64-81
|
artikel
|
| 7 |
Editorial Board
|
|
|
2019
|
95 |
C |
p. ii
|
artikel
|
| 8 |
Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis
|
Rivolta, Massimo W.
|
|
2019
|
95 |
C |
p. 38-47
|
artikel
|
| 9 |
On predicting epithelial mesenchymal transition by integrating RNA-binding proteins and correlation data via L 1/2-regularization method
|
Qiu, Yushan
|
|
2019
|
95 |
C |
p. 96-103
|
artikel
|
| 10 |
Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer
|
Niazi, M. Khalid Khan
|
|
2019
|
95 |
C |
p. 82-87
|
artikel
|
| 11 |
Predicting hospital associated disability from imbalanced data using supervised learning
|
Saarela, Mirka
|
|
2019
|
95 |
C |
p. 88-95
|
artikel
|
| 12 |
Predicting ICU readmission using grouped physiological and medication trends
|
Xue, Ye
|
|
2019
|
95 |
C |
p. 27-37
|
artikel
|
| 13 |
Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods
|
Hashemzadeh, Mahdi
|
|
2019
|
95 |
C |
p. 1-15
|
artikel
|
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