nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
1 |
A benchmark bone marrow aspirate smear dataset and a multi-scale cell detection model for the diagnosis of hematological disorders
|
Su, Jie |
|
|
90 |
C |
p. |
artikel |
2 |
A cascade and heterogeneous neural network for CT pulmonary nodule detection and its evaluation on both phantom and patient data
|
Xiao, Yi |
|
|
90 |
C |
p. |
artikel |
3 |
A consistent deep registration network with group data modeling
|
Gu, Dongdong |
|
|
90 |
C |
p. |
artikel |
4 |
A deep learning based multiscale approach to segment the areas of interest in whole slide images
|
Feng, Yanbo |
|
|
90 |
C |
p. |
artikel |
5 |
A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging
|
Hammouda, K. |
|
|
90 |
C |
p. |
artikel |
6 |
A predictive framework based on brain volume trajectories enabling early detection of Alzheimer's disease
|
Mofrad, Samaneh Abolpour |
|
|
90 |
C |
p. |
artikel |
7 |
Automated assessment of glomerulosclerosis and tubular atrophy using deep learning
|
Salvi, Massimo |
|
|
90 |
C |
p. |
artikel |
8 |
Automatic MR image quality evaluation using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging
|
Fantini, Irene |
|
|
90 |
C |
p. |
artikel |
9 |
Compressed medical imaging based on average sparsity model and reweighted analysis of multiple basis pursuit
|
Rahim, Tariq |
|
|
90 |
C |
p. |
artikel |
10 |
Computed tomography image reconstruction using stacked U-Net
|
Mizusawa, Satoru |
|
|
90 |
C |
p. |
artikel |
11 |
Corrigendum to “Machine learning techniques for mitoses classification” [Comput. Med. Imaging Graphics 87 January (2021) 101832]
|
Nofallah, Shima |
|
|
90 |
C |
p. |
artikel |
12 |
3D dissimilar-siamese-u-net for hyperdense Middle cerebral artery sign segmentation
|
You, Jia |
|
|
90 |
C |
p. |
artikel |
13 |
Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow
|
Hernandez, Soleil |
|
|
90 |
C |
p. |
artikel |
14 |
Editorial Board
|
|
|
|
90 |
C |
p. |
artikel |
15 |
Evaluation of deep learning approaches for identification of different corona-virus species and time series prediction
|
Younis, Mohammed Chachan |
|
|
90 |
C |
p. |
artikel |
16 |
Fast and efficient retinal blood vessel segmentation method based on deep learning network
|
Boudegga, Henda |
|
|
90 |
C |
p. |
artikel |
17 |
Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks
|
Abramova, Valeriia |
|
|
90 |
C |
p. |
artikel |
18 |
Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time
|
Zhang, Haoyue |
|
|
90 |
C |
p. |
artikel |
19 |
Learnable image histograms-based deep radiomics for renal cell carcinoma grading and staging
|
Hussain, Mohammad Arafat |
|
|
90 |
C |
p. |
artikel |
20 |
Lesion synthesis to improve intracranial hemorrhage detection and classification for CT images
|
Zhang, Guyue |
|
|
90 |
C |
p. |
artikel |
21 |
Pattern classification for breast lesion on FFDM by integration of radiomics and deep features
|
Zhang, Xinyu |
|
|
90 |
C |
p. |
artikel |
22 |
Recent advances in artificial intelligence for cardiac imaging
|
Yang, Guang |
|
|
90 |
C |
p. |
artikel |
23 |
Retinex model based stain normalization technique for whole slide image analysis
|
Hoque, Md. Ziaul |
|
|
90 |
C |
p. |
artikel |
24 |
Three-dimensional breast tumor segmentation on DCE-MRI with a multilabel attention-guided joint-phase-learning network
|
Qiao, Mengyun |
|
|
90 |
C |
p. |
artikel |
25 |
Towards quantitative and intuitive percutaneous tumor puncture via augmented virtual reality
|
Li, Ruotong |
|
|
90 |
C |
p. |
artikel |
26 |
Towards radiologist-level cancer risk assessment in CT lung screening using deep learning
|
Trajanovski, Stojan |
|
|
90 |
C |
p. |
artikel |
27 |
Tumor classification in automated breast ultrasound (ABUS) based on a modified extracting feature network
|
Zhuang, Zhemin |
|
|
90 |
C |
p. |
artikel |