104 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
6 papers · 1 filter
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…
Evaluating Visual Explanations of Attention Maps for Transformer-based Medical Imaging
Minjae Chung, Jong Bum Won, Ganghyun Kim +2
Although Vision Transformers (ViTs) have recently demonstrated superior performance in medical imaging problems, they face explainability issues similar to previous architectures s…
Color Flow Imaging Microscopy Improves Identification of Stress Sources of Protein Aggregates in Biopharmaceuticals
Michaela Cohrs, Shiwoo Koak, Yejin Lee +4
Protein-based therapeutics play a pivotal role in modern medicine targeting various diseases. Despite their therapeutic importance, these products can aggregate and form subvisible…
Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models
Solha Kang, Joris Vankerschaver, Utku Ozbulak
With the advancements in self-supervised learning (SSL), transformer-based computer vision models have recently demonstrated superior results compared to convolutional neural netwo…
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?
Utku Ozbulak, Esla Timothy Anzaku, Solha Kang +2
Machine learning (ML) research strongly relies on benchmarks in order to determine the relative effectiveness of newly proposed models. Recently, a number of prominent research eff…
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems
Utku Ozbulak, Baptist Vandersmissen, Azarakhsh Jalalvand +3
Given their substantial success in addressing a wide range of computer vision challenges, Convolutional Neural Networks (CNNs) are increasingly being used in smart home application…