1.3k citations · 1.7k across the 5 of their papers we have counts for
5 papers
Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CT
Rosa C. J. Kraaijveld, Marielle E. P. Philippens, Wietse S. C. Eppinga +4
Explainable artificial intelligence (XAI) is increasingly used to analyze the behavior of neural networks. Concept activation uses human-interpretable concepts to explain neural ne…
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
Bas H. M. van der Velden, Hugo J. Kuijf, Kenneth G. A. Gilhuijs +1
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis.…
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…
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…
Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities
Pim Moeskops, Jelmer M. Wolterink, Bas H. M. van der Velden +4
Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imag…