Fabio Hellmann

Fabio Hellmann

Doctoral Student

Publications

Anonymization of Faces: Technical and Legal Perspectives

Published in Datenschutz und Datensicherheit-DuD, 2024

This paper explores face anonymization techniques in the context of the General Data Protection Regulation (GDPR) amidst growing privacy concerns due to the widespread use of personal data in machine learning. We focus on unstructured data, specifically facial data, and discuss two approaches to assessing re-identification risks: the risk- based approach supported by GDPR and the zero or strict approach. Emphasizing a process-oriented perspective, we argue that face anonymization should consider the overall data processing context, including the actors involved and the measures taken, to achieve legally secure anonymization under GDPR’s stringent requirements. […]

Recommended citation: Hellmann, Fabio et al. "Anonymization of Faces: Technical and Legal Perspectives." Datenschutz und Datensicherheit-DuD. Springer Fachmedien Wiesbaden, 2024 https://link.springer.com/article/10.1007/s11623-024-1938-6

Comparison of clinical geneticist and computer visual attention in assessing genetic conditions

Published in PLoS genetics, 2024

The use of artificial intelligence (AI) for facial diagnostics is increasingly used in the genetics clinic to evaluate patients with potential genetic conditions. Current approaches focus on one type of AI called Deep Learning (DL). While DL- based facial diagnostic platforms have a high accuracy rate for many conditions, less is understood about how this technology assesses and classifies (categorizes) images, and how this compares to humans. To compare human and computer attention, we performed eye-tracking analyses of geneticist clinicians (n = 22) and non-clinicians (n = 22) who viewed images of people with 10 different genetic conditions, as well as images of unaffected individuals […]

Recommended citation: Duong, Dat et al. "Comparison of clinical geneticist and computer visual attention in assessing genetic conditions." PLoS genetics. Public Library of Science, 2024 https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1011168

The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data (preprint)

Published in PrePrint, 2023

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies […]

Recommended citation: Boulogne, Luuk et al. "The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data (preprint)." PrePrint. 2023 https://scholar.google.com/scholar?cluster=1865024985290776747&hl=en&oi=scholarr

Few-shot meta-learning for recognizing facial phenotypes of genetic disorders

Published in PrePrint, 2023

Computer vision has useful applications in precision medicine and recognizing facial phenotypes of genetic disorders is one of them. Many genetic disorders are known to affect faces’ visual appearance and geometry. Automated classification and similarity retrieval aid physicians in decision-making to diagnose possible genetic conditions as early as possible. Previous work has addressed the problem as a classification problem; however, the sparse label distribution, having few labeled samples, and huge class imbalances across categories make representation learning and generalization harder […]

Recommended citation: Sümer, Ömer et al. "Few-shot meta-learning for recognizing facial phenotypes of genetic disorders." PrePrint. IOS Press, 2023 https://ebooks.iospress.nl/doi/10.3233/SHTI230312

Ganonymization: A gan-based face anonymization framework for preserving emotional expressions

Published in ACM Transactions on Multimedia Computing, Communications and Applications, 2023

In recent years, the increasing availability of personal data has raised concerns regarding privacy and security. One of the critical processes to address these concerns is data anonymization, which aims to protect individual privacy and prevent the release of sensitive information. This research focuses on the importance of face anonymization. Therefore, we introduce GANonymization, a novel face anonymization framework with facial expression-preserving abilities […]

Recommended citation: Hellmann, Fabio et al. "Ganonymization: A gan-based face anonymization framework for preserving emotional expressions." ACM Transactions on Multimedia Computing, Communications and Applications. ACM, 2023 https://dl.acm.org/doi/abs/10.1145/3641107

Improving deep facial phenotyping for ultra-rare disorder verification using model ensembles

Published in Proceedings of the IEEE/CVF winter conference on applications of computer vision, 2023

Rare genetic disorders affect more than 6% of the global population. Reaching a diagnosis is challenging because rare disorders are very diverse. Many disorders have recognizable facial features that are hints for clinicians to diagnose patients. Previous work, such as GestaltMatcher, utilized representation vectors produced by a DCNN similar to AlexNet to match patients in high-dimensional feature space to support" unseen" ultra-rare disorders […]

Recommended citation: Hustinx, Alexander et al. "Improving deep facial phenotyping for ultra-rare disorder verification using model ensembles." Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2023 https://openaccess.thecvf.com/content/WACV2023/html/Hustinx_Improving_Deep_Facial_Phenotyping_for_Ultra-Rare_Disorder_Verification_Using_Model_WACV_2023_paper.html

Towards automated COVID-19 presence and severity classification

Published in arXiv preprint arXiv:2305.08660, 2023

COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of intensive care units, predicting the future severity of a COVID-19 patient is crucial. The presented approach follows state-of-theart techniques to aid medical professionals in these situations. It comprises an ensemble learning strategy via 5-fold cross-validation that includes transfer learning and combines pre-trained 3D-versions of ResNet34 and DenseNet121 for COVID19 classification and severity prediction respectively […]

Recommended citation: Müller, Dominik et al. "Towards automated COVID-19 presence and severity classification." arXiv preprint arXiv:2305.08660. 2023 https://arxiv.org/abs/2305.08660

NFB-03. Neurological manifestations in children and adolescents with Neurofibromatosis type 1-Implications for management and surveillance

Published in Neuro-Oncology, 2022

INTRODUCTION: We aimed to (1) characterize the spectrum of clinical phenotypes of NF1 in a random pediatric population, (2) correlate genotype with phenotypic expression for those with a genetic diagnosis, and (3) explore radiological features of NF1 in the central nervous system (CNS) by radiomics analyses to predict clinical course. METHODS: We performed a database search in the hospital information system of the University Children’s Hospital between January 2017 and December 2020 for patients with NF1 and evaluated the clinical phenotype by retrospective chart review. RESULTS: 75 children/adolescents were identified with suspicion/clinical diagnosis of NF1 (median age 10.0 years (range, 1 […]

Recommended citation: Angelova-Toshkina, Daniela et al. "NFB-03. Neurological manifestations in children and adolescents with Neurofibromatosis type 1-Implications for management and surveillance." Neuro-Oncology. Oxford University Press, 2022 https://academic.oup.com/neuro-oncology/article-abstract/24/Supplement_1/i128/6601248

Deformable dilated faster R-CNN for universal lesion detection in CT images

Published in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021

Cancer is a major public health issue and takes the second-highest toll of deaths caused by non-communicable diseases worldwide. Automatically detecting lesions at an early stage is essential to increase the chance of a cure. This study proposes a novel dilated Faster R-CNN with modulated deformable convolution and modulated deformable positive-sensitive region of interest pooling to detect lesions in computer tomography images. A pre-trained VGG-16 is transferred as the backbone of Faster R-CNN, followed by a region proposal network and a region of interest pooling layer to achieve lesion detection […]

Recommended citation: Hellmann, Fabio et al. "Deformable dilated faster R-CNN for universal lesion detection in CT images." 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021 https://ieeexplore.ieee.org/abstract/document/9631021/