activity
20182021
most citedInterpretable deep learning regression for breast density estimation on MRI

5 citations · 9 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV20211 cited

Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT

Christina B. Lund, Bas H. M. van der Velden

This paper assesses whether using clinical characteristics in addition to imaging can improve automated segmentation of kidney cancer on contrast-enhanced computed tomography (CT).…

eess.IV2021

MixLacune: Segmentation of lacunes of presumed vascular origin

Denis Kutnar, Bas H. M. van der Velden, Marta Girones Sanguesa +3

Lacunes of presumed vascular origin are fluid-filled cavities of between 3 - 15 mm in diameter, visible on T1 and FLAIR brain MRI. Quantification of lacunes relies on manual annota…

eess.IV20213 cited

MixMicrobleed: Multi-stage detection and segmentation of cerebral microbleeds

Marta Girones Sanguesa, Denis Kutnar, Bas H. M. van der Velden +1

Cerebral microbleeds are small, dark, round lesions that can be visualised on T2*-weighted MRI or other sequences sensitive to susceptibility effects. In this work, we propose a mu…

eess.IV20205 cited

Interpretable deep learning regression for breast density estimation on MRI

Bas H. M. van der Velden, Max A. A. Ragusi, Markus H. A. Janse +2

Breast density, which is the ratio between fibroglandular tissue (FGT) and total breast volume, can be assessed qualitatively by radiologists and quantitatively by computer algorit…

cs.CV2018

Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing

Bas H. M. van der Velden, Bob D. de Vos, Claudette E. Loo +3

Response of breast cancer to neoadjuvant chemotherapy (NAC) can be monitored using the change in visible tumor on magnetic resonance imaging (MRI). In our current workflow, seed po…

physics.med-ph2018

BPE and computer-extracted parenchymal enhancement for breast cancer risk, response monitoring, and prognosis

Bas H. M. van der Velden

Functional behavior of breast cancer - representing underlying biology - can be analyzed using MRI. The most widely used breast MR imaging protocol is dynamic contrast-enhanced T1-…