1.3k citations · 1.7k across the 14 of their papers we have counts for
14 papers
Colony Grounded SAM2: Zero-shot detection and segmentation of bacterial colonies using foundation models
Daan Korporaal, Patrick de Kruijf, Ralph H. G. M. Litjens +1
The detection and classification of bacterial colonies in images of agar-plates is important in microbiology, but is hindered by the lack of labeled datasets. Therefore, we propose…
Identifying environmental factors associated with tetrodotoxin contamination in bivalve mollusks using eXplainable AI
M. C. Schoppema, B. H. M. van der Velden, A. Hürriyetoğlu +4
Since 2012, tetrodotoxin (TTX) has been found in seafoods such as bivalve mollusks in temperate European waters. TTX contamination leads to food safety risks and economic losses, m…
Food safety trends across Europe: insights from the 392-million-entry CompreHensive European Food Safety (CHEFS) database
Nehir Kizililsoley, Floor van Meer, Osman Mutlu +9
In the European Union, official food safety monitoring data collected by member states are submitted to the European Food Safety Authority (EFSA) and published on Zenodo. This data…
BrightCookies at SemEval-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification
Foteini Papadopoulou, Osman Mutlu, Neris Özen +3
This paper presents our system developed for the SemEval-2025 Task 9: The Food Hazard Detection Challenge. The shared task's objective is to evaluate explainable classification sys…
Federated learning in food research
Zuzanna Fendor, Bas H. M. van der Velden, Xinxin Wang +3
Research in the food domain is at times limited due to data sharing obstacles, such as data ownership, privacy requirements, and regulations. While important, these obstacles can r…
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…
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).…
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…
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…
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-…
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…