47 citations · 65 across the 8 of their papers we have counts for
8 papers
Region-Weighted Losses and Model Fusion for Cross-Modal PET Attenuation Correction
Khoa Tuan Nguyen, Joris Vankerschaver, Wesley De Neve
We describe our approach to the Big Cross-Modal Attenuation Correction (BIC-MAC) challenge, which asks for a pseudo-CT in Hounsfield Units to be synthesized from Non-Attenuation-Co…
Token-Based Detection of Spurious Correlations in Vision Transformers
Solha Kang, Esla Timothy Anzaku, Wesley De Neve +4
Due to their powerful feature association capabilities, neural network-based computer vision models have the ability to detect and exploit unintended patterns within the data, pote…
Improved Sub-Visible Particle Classification in Flow Imaging Microscopy via Generative AI-Based Image Synthesis
Utku Ozbulak, Michaela Cohrs, Hristo L. Svilenov +2
Sub-visible particle analysis using flow imaging microscopy combined with deep learning has proven effective in identifying particle types, enabling the distinction of harmless com…
Leveraging Human-Machine Interactions for Computer Vision Dataset Quality Enhancement
Esla Timothy Anzaku, Hyesoo Hong, Jin-Woo Park +6
Large-scale datasets for single-label multi-class classification, such as \emph{ImageNet-1k}, have been instrumental in advancing deep learning and computer vision. However, a crit…
Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos using NeRFs
Khoa Tuan Nguyen, Francesca Tozzi, Nikdokht Rashidian +3
Given that a conventional laparoscope only provides a two-dimensional (2-D) view, the detection and diagnosis of medical ailments can be challenging. To overcome the visual constra…
Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training
Utku Ozbulak, Hyun Jung Lee, Beril Boga +5
Although supervised learning has been highly successful in improving the state-of-the-art in the domain of image-based computer vision in the past, the margin of improvement has di…
Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks
Utku Ozbulak, Esla Timothy Anzaku, Wesley De Neve +1
Although the adoption rate of deep neural networks (DNNs) has tremendously increased in recent years, a solution for their vulnerability against adversarial examples has not yet be…
Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks
Fréderic Godin, Jonas Degrave, Joni Dambre +1
In this paper, we introduce a novel type of Rectified Linear Unit (ReLU), called a Dual Rectified Linear Unit (DReLU). A DReLU, which comes with an unbounded positive and negative…