| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
AAR-RT – A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases
|
Wu, Xingyu
|
|
2019
|
54 |
C |
p. 45-62
|
artikel
|
| 2 |
A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning
|
Agn, Mikael
|
|
2019
|
54 |
C |
p. 220-237
|
artikel
|
| 3 |
An image interpolation approach for acquisition time reduction in navigator-based 4D MRI
|
Karani, Neerav
|
|
2019
|
54 |
C |
p. 20-29
|
artikel
|
| 4 |
BIRNet: Brain image registration using dual-supervised fully convolutional networks
|
Fan, Jingfan
|
|
2019
|
54 |
C |
p. 193-206
|
artikel
|
| 5 |
Breast MRI and X-ray mammography registration using gradient values
|
García, Eloy
|
|
2019
|
54 |
C |
p. 76-87
|
artikel
|
| 6 |
Constrained-CNN losses for weakly supervised segmentation
|
Kervadec, Hoel
|
|
2019
|
54 |
C |
p. 88-99
|
artikel
|
| 7 |
CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation
|
Wang, Shuai
|
|
2019
|
54 |
C |
p. 168-178
|
artikel
|
| 8 |
Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising
|
Liu, Peng
|
|
2019
|
54 |
C |
p. 306-315
|
artikel
|
| 9 |
Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network
|
Lu, Donghuan
|
|
2019
|
54 |
C |
p. 100-110
|
artikel
|
| 10 |
DeepPET: A deep encoder–decoder network for directly solving the PET image reconstruction inverse problem
|
Häggström, Ida
|
|
2019
|
54 |
C |
p. 253-262
|
artikel
|
| 11 |
Discovering hierarchical common brain networks via multimodal deep belief network
|
Zhang, Shu
|
|
2019
|
54 |
C |
p. 238-252
|
artikel
|
| 12 |
Editorial Board
|
|
|
2019
|
54 |
C |
p. ii
|
artikel
|
| 13 |
Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI
|
Sun, Jiaqi
|
|
2019
|
54 |
C |
p. 122-137
|
artikel
|
| 14 |
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
|
Schlegl, Thomas
|
|
2019
|
54 |
C |
p. 30-44
|
artikel
|
| 15 |
Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI
|
Torrents-Barrena, Jordina
|
|
2019
|
54 |
C |
p. 263-279
|
artikel
|
| 16 |
Graph Convolutions on Spectral Embeddings for Cortical Surface Parcellation
|
Gopinath, Karthik
|
|
2019
|
54 |
C |
p. 297-305
|
artikel
|
| 17 |
Integrating spatial configuration into heatmap regression based CNNs for landmark localization
|
Payer, Christian
|
|
2019
|
54 |
C |
p. 207-219
|
artikel
|
| 18 |
Medical image classification using synergic deep learning
|
Zhang, Jianpeng
|
|
2019
|
54 |
C |
p. 10-19
|
artikel
|
| 19 |
Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
|
Cheplygina, Veronika
|
|
2019
|
54 |
C |
p. 280-296
|
artikel
|
| 20 |
OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions
|
Heinrich, Mattias P.
|
|
2019
|
54 |
C |
p. 1-9
|
artikel
|
| 21 |
Optimal surface segmentation with convex priors in irregularly sampled space
|
Shah, Abhay
|
|
2019
|
54 |
C |
p. 63-75
|
artikel
|
| 22 |
Patient-attentive sequential strategy for perimetry-based visual field acquisition
|
Kucur, Şerife Seda
|
|
2019
|
54 |
C |
p. 179-192
|
artikel
|
| 23 |
Population shrinkage of covariance (PoSCE) for better individual brain functional-connectivity estimation
|
Rahim, Mehdi
|
|
2019
|
54 |
C |
p. 138-148
|
artikel
|
| 24 |
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge
|
Veta, Mitko
|
|
2019
|
54 |
C |
p. 111-121
|
artikel
|
| 25 |
Ultrasound guidance in minimally invasive robotic procedures
|
Antico, Maria
|
|
2019
|
54 |
C |
p. 149-167
|
artikel
|
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