Dr.-Ing. Zeyd Boukhers

Lead of FAIR Data & Distributed Analytics Group

Fraunhofer Institute for Applied Information Technology (FIT)

Institute for Medical Informatics, University Hospital of Cologne

Leading researcher in FAIR data infrastructure and distributed analytics with dual appointments at Fraunhofer FIT and University Hospital of Cologne. Co-Spokesperson of Germany's largest data science research consortium NFDI4DataScience. Expertise spans web-based data spaces, semantic interoperability, and AI-enhanced data management systems.

Publications Research Projects Research Group Contact

Research Interests

Leading the FAIR Data & Distributed Analytics Group at Fraunhofer FIT

FAIR Data

Data management, FAIR principles, data spaces

Machine Learning

Deep learning, neural networks, AI applications

NLP

Natural language processing, text mining

Data Sovereignty

Data governance, privacy, security

Recent News

December 01, 2024

Leading Multiple AI and Data Science Projects

Currently managing over €6M in research funding across 10+ active projects including NFDI4DS, FAIR Data Spaces, and ARGUS cultural heritage preservation initiative.

November 01, 2024

New Papers Accepted for 2025 Conferences

Several papers accepted at top-tier venues including AIME 2025, ICMR 2025, and ESWC workshops, covering LLM applications and multimodal AI systems.

June 01, 2024

Co-leading NFDI4DS as Co-Spokesperson

Continuing leadership role in Germany's largest data science research infrastructure initiative, connecting 17 institutions nationwide with €12M total funding.

Learn more

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Selected Publications

2024

1. BladeView: Towards Automatic Wind Turbine Inspection with Unmanned Aerial Vehicle

   Yang, Cong , Zhou, Hua , Liu, Xun , Ke, Yan , Gao, Bo , Grzegorzek, Marcin , Boukhers, Zeyd , Chen, Tao , See, John

   IEEE Transactions on Automation Science and Engineering 22 7530-7545 2024

   Link DOI

2. A non-enhanced CT based deep learning diagnostic system for high-risk COVID-19 infection in lung cancer patients

   Du, Tianming , Sun, Yihao , Wang, Xinghao , Jiang, Tao , Xu, Ning , Boukhers, Zeyd , Grzegorzek, Marcin , Sun, Hongzan , Li, Chen

   Frontiers in Medicine 11 1444708 2024

   Link DOI

   Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection. This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6. The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6. Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

3. Context-agnostic data visitation to exchange health data for scientific research: the example of PADME PHT

   Hallinan, Dara , Nestor, Marc , Akhtar, Muhammad Hamza , Breß, Constantin , Jaberansary, Mehrshad , Lange, Christoph , Maia, Macedo , Molnár-Gábor, Fruzsina , Yediel, Yeliz Ucer , Boukhers, Zeyd

   International Data Privacy Law 2024

   Link DOI

4. R4-SLAM: Toward Real-time, Robust, and Resource-Restricted Visual SLAM in Dynamic Environments

   Zhang, Zhijie , Yang, Cong , Qin, Xue , Sui, Wei , Boukhers, Zeyd , Kong, Hui , Liu, Jie , Li, Zhijun

   IEEE Transactions on Instrumentation and Measurement 2024

2023

1. Skeleton ground truth extraction: methodology, annotation tool and benchmarks

   Yang, Cong , Indurkhya, Bipin , See, John , Gao, Bo , Ke, Yan , Boukhers, Zeyd , Yang, Zhenyu , Grzegorzek, Marcin

   International Journal of Computer Vision 132 1219-1241 2023

   Link DOI

   Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with the popularity of deep learning techniques. Furthermore, we see skeleton GTs used not only for training skeleton detectors with Convolutional Neural Networks (CNN), but also for evaluating skeleton-related pruning and matching algorithms. However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards. As a result, it is difficult to evaluate and reproduce CNN-based skeleton detectors and algorithms on a fair basis. In this paper, we present a heuristic strategy for object skeleton GT extraction in binary shapes and natural images. Our strategy is built on an extended theory of diagnosticity hypothesis, which enables encoding human-in-the-loop GT extraction based on clues from the target’s context, simplicity, and completeness. Using this strategy, we developed a tool, SkeView, to generate skeleton GT of 17 existing shape and image datasets. The GTs are then structurally evaluated with representative methods to build viable baselines for fair comparisons. Experiments demonstrate that GTs generated by our strategy yield promising quality with respect to standard consistency, and also provide a balance between simplicity and completeness.

2. Knowledge guided multi-filter residual convolutional neural network for ICD coding from clinical text

   Boukhers, Zeyd , Goswami, Prantik , Jürjens, Jan

   Neural Computing and Applications 2023

   Link DOI

   One challenge often encountered when using Deep Neural Network models for automatic ICD coding is their potential inability to effectively handle unseen clinical texts, especially when these models are only trained on a limited number of examples. This is because these models rely solely on the patterns and relationships present in the training data, and may not be able to effectively incorporate additional knowledge about the relationships between medical entities. To address this issue, we introduce KG-MultiResCNN - Knowledge Guided Multi-filter Residual Convolutional Neural Network model, which combines training examples with external knowledge from the Wikidata Knowledge Graph (KG) in order to better capture the relationships between medical entities. The KG is a structured database that contains a wealth of information about various entities, including medical concepts and their relationships with one another. By incorporating this external knowledge into our model, we are able to improve its ability to predict ICD codes for new clinical texts. In our experiments with the MIMIC-III dataset, we found that the KG-MultiResCNN model significantly outperformed the baseline approaches. This demonstrates the effectiveness of using external knowledge, in addition to training examples, to improve the performance of deep learning models for automatic ICD coding.

3. Deep author name disambiguation using DBLP data

   Boukhers, Zeyd , Asundi, Nagaraj Bahubali

   International Journal on Digital Libraries 25 431-441 2023

   Link DOI

   In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.

2022

1. COIN: Counterfactual Image Generation for VQA Interpretation

   Boukhers, Zeyd , Hartmann, Timo , Jürjens, Jan

   Sensors 22 2245 2022

   Link DOI

   Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced. However, they are still error-prone when dealing with relatively complex questions. Therefore, it is important to understand the behaviour of the VQA models before adopting their results. In this paper, we introduce an interpretability approach for VQA models by generating counterfactual images. Specifically, the generated image is supposed to have the minimal possible change to the original image and leads the VQA model to give a different answer. In addition, our approach ensures that the generated image is realistic. Since quantitative metrics cannot be employed to evaluate the interpretability of the model, we carried out a user study to assess different aspects of our approach. In addition to interpreting the result of VQA models on single images, the obtained results and the discussion provides an extensive explanation of VQA models’ behaviour.

2020

1. Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy

   Koltsov, Sergei , Ignatenko, Vera , Boukhers, Zeyd , Staab, Steffen

   Entropy 22 394 2020

   Link DOI

   Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models—Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)—we, first of all, show that the minimum of Renyi entropy coincides with the “true” number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.

2019

1. Fractal approach for determining the optimal number of topics in the field of topic modeling

   Koltsov, Sergei , Ignatenko, Vera , Boukhers, Zeyd , Staab, Steffen

   Journal of Physics: Conference Series 1163 012025 2019

   Link DOI

2017

1. Example-based 3D trajectories of objects from 2D videos

   Boukhers, Zeyd , Shirahama, Kimiaki , Grzegorzek, Marcin

   IEEE Transactions on Circuits and Systems for Video Technology 28 2246-2260 2017

   Link DOI

2. Less Restrictive Camera Odometry from Monocular Camera

   Boukhers, Zeyd , Shirahama, Kimiaki , Grzegorzek, Marcin

   Multimedia Tools and Applications 77 16199-16222 2017

   Link DOI

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