Research Explorer

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Researcher

Utku Özbulak

Awards & Distinctions

19 Results

2025

Color flow imaging microscopy improves identification of stress sources of protein aggregates in biopharmaceuticals

Michaela CohrsShiwoo KoakYejin LeeYu Jin SungWesley De NeveHristo SvilenovUtku Özbulak

Evaluating visual explanations of attention maps for transformer-based medical imaging

Minjae ChungJong Bum WonGanghyun KimYujin KimUtku Özbulak

Exploring patient data requirements in training effective AI models for MRI-based breast cancer classification

Solha KangWesley De NeveFrancois RameauUtku Özbulak

2024

Assessing the reliability of point mutation as data augmentation for deep learning with genomic data

Hyunjung LeeUtku ÖzbulakHomin ParkStephen DepuydtWesley De NeveJoris Vankerschaver

A1 Journal Article in BMC BIOINFORMATICS

Identifying critical tokens for accurate predictions in transformer-based medical imaging models

Solha KangJoris VankerschaverUtku Özbulak

2023

Know your self-supervised learning : a survey on image-based generative and discriminative training

Utku ÖzbulakHyun Jung LeeBeril BogaEsla Timothy AnzakuHomin ParkArnout Van MessemWesley De NeveJoris Vankerschaver

A2 Journal Article in TRANSACTIONS ON MACHINE LEARNING RESEARCH

Mutate and observe : utilizing deep neural networks to investigate the impact of mutations on translation initiation

Utku ÖzbulakHyun Jung LeeJasper ZuallaertWesley De NeveStephen DepuydtJoris Vankerschaver

A1 Journal Article in BIOINFORMATICS

Tryp : a dataset of microscopy images of unstained thick blood smears for trypanosome detection

Esla Timothy AnzakuMohammed Aliy MohammedUtku ÖzbulakJongbum WonHyesoo HongJanarthanan KrishnamoorthySofie Van HoeckeStefan MagezArnout Van MessemWesley De Neve

A1 Journal Article in SCIENTIFIC DATA

2022

Prevalence of adversarial examples in neural networks : attacks, defenses, and opportunities

Utku ÖzbulakWesley De NeveArnout Van Messem

Utilizing mutations to evaluate interpretability of neural networks on genomic data

Utku ÖzbulakSolha KangJasper ZuallaertStephen DepuydtJoris Vankerschaver

2021

Automatic detection of Trypanosomosis in thick blood smears using image pre-processing and deep learning

Taewoo JungEsla Timothy AnzakuUtku ÖzbulakStefan MagezArnout Van MessemWesley De Neve

Evaluating adversarial attacks on ImageNet : a reality check on misclassification classes

Utku ÖzbulakMaura PintorArnout Van MessemWesley De Neve

Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems

Utku ÖzbulakBaptist VandersmissenAzarakhsh JalalvandIvo CouckuytArnout Van MessemWesley De Neve

A1 Journal Article in COMPUTER VISION AND IMAGE UNDERSTANDING

Selection of source images heavily influences the effectiveness of adversarial attacks

Utku ÖzbulakEsla Timothy AnzakuWesley De NeveArnout Van Messem

2020

Perturbation analysis of gradient-based adversarial attacks

Utku ÖzbulakManvel GasparyanWesley De NeveArnout Van Messem

A1 Journal Article in PATTERN RECOGNITION LETTERS

Regional image perturbation reduces Lp norms of adversarial examples while maintaining model-to-model transferability

Utku ÖzbulakJonathan PeckWesley De NeveBart GoossensYvan SaeysArnout Van Messem

2019

Impact of adversarial examples on deep learning models for biomedical image segmentation

Utku ÖzbulakArnout Van MessemWesley De Neve

Not all adversarial examples require a complex defense : identifying over-optimized adversarial examples with IQR-based logit thresholding

Utku ÖzbulakArnout Van MessemWesley De Neve

2018

How the Softmax output is misleading for evaluating the strength of adversarial examples

Utku ÖzbulakWesley De NeveArnout Van Messem