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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…
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…
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…
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…