24 citations · 46 across the 16 of their papers we have counts for
17 papers
SpurBreast: A Curated Dataset for Investigating Spurious Correlations in Real-world Breast MRI Classification
Jong Bum Won, Wesley De Neve, Joris Vankerschaver +1
Deep neural networks (DNNs) have demonstrated remarkable success in medical imaging, yet their real-world deployment remains challenging due to spurious correlations, where models…
Toward Using Machine Learning as a Shape Quality Metric for Liver Point Cloud Generation
Khoa Tuan Nguyen, Gaeun Oh, Ho-min Park +5
While 3D medical shape generative models such as diffusion models have shown promise in synthesizing diverse and anatomically plausible structures, the absence of ground truth make…
FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning
Taehwan Yoon, Bongjun Choi, Wesley De Neve
Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, data and system heterogeneity often cause catastroph…
One Patient's Annotation is Another One's Initialization: Towards Zero-Shot Surgical Video Segmentation with Cross-Patient Initialization
Seyed Amir Mousavi, Utku Ozbulak, Francesca Tozzi +4
Video object segmentation is an emerging technology that is well-suited for real-time surgical video segmentation, offering valuable clinical assistance in the operating room by en…
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…
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…
The Impact of the Single-Label Assumption in Image Recognition Benchmarking
Esla Timothy Anzaku, Seyed Amir Mousavi, Arnout Van Messem +1
Deep neural networks (DNNs) are typically evaluated under the assumption that each image has a single correct label. However, many images in benchmarks like ImageNet contain multip…
A Principled Evaluation Protocol for Comparative Investigation of the Effectiveness of DNN Classification Models on Similar-but-non-identical Datasets
Esla Timothy Anzaku, Haohan Wang, Arnout Van Messem +1
Deep Neural Network (DNN) models are increasingly evaluated using new replication test datasets, which have been carefully created to be similar to older and popular benchmark data…
Exact Feature Collisions in Neural Networks
Utku Ozbulak, Manvel Gasparyan, Shodhan Rao +2
Predictions made by deep neural networks were shown to be highly sensitive to small changes made in the input space where such maliciously crafted data points containing small pert…
Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes
Utku Ozbulak, Maura Pintor, Arnout Van Messem +1
Although ImageNet was initially proposed as a dataset for performance benchmarking in the domain of computer vision, it also enabled a variety of other research efforts. Adversaria…
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…
Regional Image Perturbation Reduces Norms of Adversarial Examples While Maintaining Model-to-model Transferability
Utku Ozbulak, Jonathan Peck, Wesley De Neve +3
Regional adversarial attacks often rely on complicated methods for generating adversarial perturbations, making it hard to compare their efficacy against well-known attacks. In thi…
Perturbation Analysis of Gradient-based Adversarial Attacks
Utku Ozbulak, Manvel Gasparyan, Wesley De Neve +1
After the discovery of adversarial examples and their adverse effects on deep learning models, many studies focused on finding more diverse methods to generate these carefully craf…
Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
Utku Ozbulak, Arnout Van Messem, Wesley De Neve
Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples…
Web Applicable Computer-aided Diagnosis of Glaucoma Using Deep Learning
Mijung Kim, Olivier Janssens, Ho-min Park +3
Glaucoma is a major eye disease, leading to vision loss in the absence of proper medical treatment. Current diagnosis of glaucoma is performed by ophthalmologists who are often ana…
Interpretable Convolutional Neural Networks for Effective Translation Initiation Site Prediction
Jasper Zuallaert, Mijung Kim, Yvan Saeys +1
Thanks to rapidly evolving sequencing techniques, the amount of genomic data at our disposal is growing increasingly large. Determining the gene structure is a fundamental requirem…
Improving Language Modeling using Densely Connected Recurrent Neural Networks
Fréderic Godin, Joni Dambre, Wesley De Neve
In this paper, we introduce the novel concept of densely connected layers into recurrent neural networks. We evaluate our proposed architecture on the Penn Treebank language modeli…