@inproceedings{ThelenVolbertNunes, author = {Thelen, Simon and Volbert, Klaus and Nunes, Danilo Weber}, title = {A Survey on Algorithmic Problems in Wireless Systems}, series = {Proceedings of the 12th International Conference on Sensor Networks (SENSORNETS), Vol 1, Feb 23, 2023 - Feb 24, 2023, Lisbon, Portugal}, booktitle = {Proceedings of the 12th International Conference on Sensor Networks (SENSORNETS), Vol 1, Feb 23, 2023 - Feb 24, 2023, Lisbon, Portugal}, publisher = {SCITEPRESS - Science and Technology Publications}, isbn = {978-989-758-635-4}, doi = {10.5220/0011791200003399}, pages = {101 -- 111}, abstract = {Considering the ongoing growth of Wireless Sensor Networks (WSNs) and the challenges they pose due to their hardware limitations as well as the intrinsic complexity of their interactions, specialized algorithms have the potential to help solving these challenges. We present a survey on recent developments regarding algorithmic problems which have applications in wireless systems and WSNs in particular. Focusing on the intersection between WSNs and algorithms, we give an overview of recent results inside this intersection, concerning topics such as routing, interference minimization, latency reduction, localization among others. Progress on solving these problems could be potentially beneficial for the industry as a whole by increasing network throughput, reducing latency or making systems more energy-efficient. We summarize and structure these recent developments and list interesting open problems to be investigated in future works.}, language = {en} } @inproceedings{NunesVolbert, author = {Nunes, Danilo Weber and Volbert, Klaus}, title = {A Wireless Low-power System for Digital Identification of Examinees (Including Covid-19 Checks)}, series = {Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS), 07.02.2022 - 08.02.2022}, booktitle = {Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS), 07.02.2022 - 08.02.2022}, publisher = {SCITEPRESS - Science and Technology Publications}, isbn = {978-989-758-551-7}, doi = {10.5220/0010912800003118}, pages = {51 -- 59}, abstract = {Indoor localization has been, for the past decade, a subject under intense development. There is, however, no currently available solution that covers all possible scenarios. Received Signal Strength Indicator (RSSI) based methods, although the most widely researched, still suffer from problems due to environment noise. In this paper, we present a system using Bluetooth Low Energy (BLE) beacons attached to the desks to localize students in exam rooms and, at the same time, automatically register them for the given exam. By using Kalman Filters (KFs) and discretizing the location task, the presented solution is capable of achieving 100\% accuracy within a distance of 45cm from the center of the desk. As the pandemic gets more controlled, with our lives slowly transitioning back to normal, there are still sanitary measures being applied. An example being the necessity to show a certification of vaccination or previous disease. Those certifications need to be manually checked for everyone entering the university's building, which requires time and staff. With that in mind, the automatic check for Covid certificates feature is also built into our system.}, language = {en} } @inproceedings{ThelenEderMelzeretal., author = {Thelen, Simon and Eder, Friedrich and Melzer, Matthias and Nunes, Danilo Weber and Stadler, Michael and Rechenauer, Christian and Obergrießer, Mathias and Jubeh, Ruben and Volbert, Klaus and D{\"u}nnweber, Jan}, title = {A Slim Digital Twin For A Smart City And Its Residents}, series = {SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology, 2023, Hi Chi Minh, Vietnam}, booktitle = {SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology, 2023, Hi Chi Minh, Vietnam}, publisher = {ACM}, isbn = {979-8-4007-0891-6}, doi = {10.1145/3628797.3628936}, pages = {8 -- 15}, abstract = {In the engineering domain, representing real-world objects using a body of data, called a digital twin, which is frequently updated by "live" measurements, has shown various advantages over tradi- tional modelling and simulation techniques. Consequently, urban planners have a strong interest in digital twin technology, since it provides them with a laboratory for experimenting with data before making far-reaching decisions. Realizing these decisions involves the work of professionals in the architecture, engineering and construction (AEC) domain who nowadays collaborate via the methodology of building information modeling (BIM). At the same time, the citizen plays an integral role both in the data acquisition phase, while also being a beneficiary of the improved resource management strategies. In this paper, we present a prototype for a "digital energy twin" platform we designed in cooperation with the city of Regensburg. We show how our extensible platform de- sign can satisfy the various requirements of multiple user groups through a series of data processing solutions and visualizations, in- dicating valuable design and implementation guidelines for future projects. In particular, we focus on two example use cases concern- ing building electricity monitoring and BIM. By implementing a flexible data processing architecture we can involve citizens in the data acquisition process, meeting the demands of modern users regarding maximum transparency in the handling of their data.}, language = {en} } @misc{ScheppachNunesArizietal., author = {Scheppach, Markus W. and Nunes, Danilo Weber and Arizi, X. and Rauber, David and Probst, Andreas and Nagl, Sandra and R{\"o}mmele, Christoph and Meinikheim, Michael and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Procedural phase recognition in endoscopic submucosal dissection (ESD) using artificial intelligence (AI)}, series = {Endoscopy}, volume = {56}, journal = {Endoscopy}, number = {S 02}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0044-1783804}, pages = {S439}, abstract = {Aims Recent evidence suggests the possibility of intraprocedural phase recognition in surgical operations as well as endoscopic interventions such as peroral endoscopic myotomy and endoscopic submucosal dissection (ESD) by AI-algorithms. The intricate measurement of intraprocedural phase distribution may deepen the understanding of the procedure. Furthermore, real-time quality assessment as well as automation of reporting may become possible. Therefore, we aimed to develop an AI-algorithm for intraprocedural phase recognition during ESD. Methods A training dataset of 364385 single images from 9 full-length ESD videos was compiled. Each frame was classified into one procedural phase. Phases included scope manipulation, marking, injection, application of electrical current and bleeding. Allocation of each frame was only possible to one category. This training dataset was used to train a Video Swin transformer to recognize the phases. Temporal information was included via logarithmic frame sampling. Validation was performed using two separate ESD videos with 29801 single frames. Results The validation yielded sensitivities of 97.81\%, 97.83\%, 95.53\%, 85.01\% and 87.55\% for scope manipulation, marking, injection, electric application and bleeding, respectively. Specificities of 77.78\%, 90.91\%, 95.91\%, 93.65\% and 84.76\% were measured for the same parameters. Conclusions The developed algorithm was able to classify full-length ESD videos on a frame-by-frame basis into the predefined classes with high sensitivities and specificities. Future research will aim at the development of quality metrics based on single-operator phase distribution.}, language = {en} } @misc{ScheppachWeberNunesArizietal., author = {Scheppach, Markus W. and Weber Nunes, Danilo and Arizi, X. and Rauber, David and Probst, Andreas and Nagl, Sandra and R{\"o}mmele, Christoph and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Single frame workflow recognition during endoscopic submucosal dissection (ESD) using artificial intelligence (AI)}, series = {Endoscopy}, volume = {57}, journal = {Endoscopy}, number = {S 02}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0045-1806324}, pages = {S511}, abstract = {Aims Precise surgical phase recognition and evaluation may improve our understanding of complex endoscopic procedures. Furthermore, quality control measurements and endoscopy training could benefit from objective descriptions of surgical phase distributions. Therefore, we aimed to develop an artificial intelligence algorithm for frame-by-frame operational phase recognition during endoscopic submucosal dissection (ESD). Methods Full length ESD-videos from 31 patients comprising 6.297.782 single images were collected retrospectively. Videos were annotated on a frame-by-frame basis for the operational macro-phases diagnostics, marking, injection, dissection and bleeding. Further subphases were the application of electrical current, visible injection of fluid into the submucosal space and scope manipulation, leading to 11 phases in total. 4.975.699 frames (21 patients) were used for training of a video swin transformer using uniform frame sampling for temporal information. Hyperparameter tuning was performed with 897.325 further frames (6 patients), while 424.758 frames (4 patients) were used for validation. Results The overall F1 scores on the test dataset for the macro-phases and all 11 phases were 0.96 and 0.90, respectively. The recall values for diagnostics, marking, injection, dissection and bleeding were 1.00, 1.00, 0.95, 0.96 and 0.93, respectively. Conclusions The algorithm classified operational phases during ESD with high accuracy. A precise evaluation of phase distribution may allow for the development of objective quality metrics for quality control and training.}, language = {en} } @inproceedings{NunesHammerHammeretal., author = {Nunes, Danilo Weber and Hammer, Michael and Hammer, Simone and Uller, Wibke and Palm, Christoph}, title = {Classification of Vascular Malformations Based on T2 STIR Magnetic Resonance Imaging}, series = {Bildverarbeitung f{\"u}r die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022}, publisher = {Springer Vieweg}, address = {Wiesbaden}, doi = {10.1007/978-3-658-36932-3_57}, pages = {267 -- 272}, abstract = {Vascular malformations (VMs) are a rare condition. They can be categorized into high-flow and low-flow VMs, which is a challenging task for radiologists. In this work, a very heterogeneous set of MRI images with only rough annotations are used for classification with a convolutional neural network. The main focus is to describe the challenging data set and strategies to deal with such data in terms of preprocessing, annotation usage and choice of the network architecture. We achieved a classification result of 89.47 \% F1-score with a 3D ResNet 18.}, language = {en} } @article{MaerklRueckertRauberetal., author = {Maerkl, Raphaela and Rueckert, Tobias and Rauber, David and Gutbrod, Max and Weber Nunes, Danilo and Palm, Christoph}, title = {Enhancing generalization in zero-shot multi-label endoscopic instrument classification}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {20}, journal = {International Journal of Computer Assisted Radiology and Surgery}, publisher = {Springer Nature}, doi = {10.1007/s11548-025-03439-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-85674}, pages = {1577 -- 1587}, abstract = {Purpose Recognizing previously unseen classes with neural networks is a significant challenge due to their limited generalization capabilities. This issue is particularly critical in safety-critical domains such as medical applications, where accurate classification is essential for reliability and patient safety. Zero-shot learning methods address this challenge by utilizing additional semantic data, with their performance relying heavily on the quality of the generated embeddings. Methods This work investigates the use of full descriptive sentences, generated by a Sentence-BERT model, as class representations, compared to simpler category-based word embeddings derived from a BERT model. Additionally, the impact of z-score normalization as a post-processing step on these embeddings is explored. The proposed approach is evaluated on a multi-label generalized zero-shot learning task, focusing on the recognition of surgical instruments in endoscopic images from minimally invasive cholecystectomies. Results The results demonstrate that combining sentence embeddings and z-score normalization significantly improves model performance. For unseen classes, the AUROC improves from 43.9\% to 64.9\%, and the multi-label accuracy from 26.1\% to 79.5\%. Overall performance measured across both seen and unseen classes improves from 49.3\% to 64.9\% in AUROC and from 37.3\% to 65.1\% in multi-label accuracy, highlighting the effectiveness of our approach. Conclusion These findings demonstrate that sentence embeddings and z-score normalization can substantially enhance the generalization performance of zero-shot learning models. However, as the study is based on a single dataset, future work should validate the method across diverse datasets and application domains to establish its robustness and broader applicability.}, language = {en} } @misc{ScheppachWeberNunesRauberetal., author = {Scheppach, Markus W. and Weber Nunes, Danilo and Rauber, David and Arizi, X. and Probst, Andreas and Nagl, Sandra and R{\"o}mmele, Christoph and Ebigbo, Alanna and Palm, Christoph and Messmann, Helmut}, title = {K{\"u}nstliche Intelligenz-basierte Erkennung von interventionellen Phasen bei der endoskopischen Submukosadissektion}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {63}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {08}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0045-1811093}, pages = {e612 -- e613}, abstract = {Einleitung: Die endoskopische Submukosadissektion (ESD) ist ein komplexes endoskopisches Verfahren, das technische Expertise erfordert. Objektive Methoden zur Analyse von interventionellen Abl{\"a}ufen bei ESD k{\"o}nnten f{\"u}r Qualit{\"a}tssicherung und Ausbildung, wie auch eine automatische Befunderstellung von Nutzen sein. Ziele: In dieser Studie wurde ein KI-Algorithmus f{\"u}r die Erkennung und Klassifizierung der interventionellen Phasen der ESD entwickelt, um die technische Basis f{\"u}r eine standardisierte Leistungsbewertung und automatische Befunderstellung zu schaffen. Methodik: Vollst{\"a}ndige ESD-Videoaufnahmen von 49 Patienten wurden retrospektiv zusammengestellt. Der Datensatz umfasste 6.390.151 Einzelbilder, die alle f{\"u}r die folgenden interventionellen Phasen annotiert wurden: Diagnostik, Markierung, Injektion, Dissektion und H{\"a}mostase. 3.973.712 Bilder (28 Patienten) wurden f{\"u}r das Training eines Video-Swin-Transformers genutzt. Dabei wurde temporale Information durch standardisierte BIldextraktion in festgelegten zeitlichen Abst{\"a}nden zum analysierten Bild inkorporiert. 2.416.439 separate Bilder (21 Patienten) wurden f{\"u}r eine interne Validierung genutzt. Ergebnis: Bei der internen Evaluation erreichte das System insgesamt einen F1-Wert von 0,88. Es wurden F1-Werte von 0,99, 0,89, 0,89, 0,91 und 0,52 f{\"u}r Diagnostik, Markierung, Injektion, Dissektion bzw. Blutungsmanagement gemessen. Die Sensitivit{\"a}ten f{\"u}r dieselben Parameter betrugen 1,00, 0,80, 0,94, 0,89 und 0,67, die Spezifit{\"a}ten lagen bei 1,00, 1,00, 0,98, 0,88 und 0,93. Positive pr{\"a}diktive Werte wurden mit 0,98, 1,00, 0,85, 0,94 und 0,43 gemessen. Schlussfolgerung: In dieser vorl{\"a}ufigen Studie zeigte ein KI-Algorithmus eine hohe Leistungsf{\"a}higkeit f{\"u}r die Einzelbild-Erkennung von Verfahrensphasen w{\"a}hrend der ESD. Die vergleichsweise niedrige Leistung f{\"u}r die Blutungsphase wurde auf das seltene Auftreten von Blutungsepisoden im Trainingsdatensatz zur{\"u}ckgef{\"u}hrt, der zu diesem Zeitpunkt nur Videos in voller L{\"a}nge umfasste. Die zuk{\"u}nftige Entwicklung des Algorithmus wird sich auf die Reduzierung von Klassenungleichgewichten durch selektive Annotationsprotokolle konzentrieren.}, language = {de} } @misc{ScheppachRauberZingleretal., author = {Scheppach, Markus W. and Rauber, David and Zingler, C. and Weber Nunes, Danilo and Probst, Andreas and R{\"o}mmele, Christoph and Nagl, Sandra and Ebigbo, Alanna and Palm, Christoph and Messmann, Helmut}, title = {Instrumentenerkennung w{\"a}hrend der endoskopischen Submukosadissektion mittels k{\"u}nstlicher Intelligenz}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {63}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {8}, publisher = {Thieme}, doi = {10.1055/s-0045-1811092}, abstract = {Einleitung: Die endoskopische Submukosadissektion (ESD) ist eine komplexe Technik zur Resektion gastrointestinaler Fr{\"u}hneoplasien. Dabei werden f{\"u}r die verschiedenen Schritte der Intervention spezifische endoskopische Instrumente verwendet. Die pr{\"a}zise und automatische Erkennung und Abgrenzung der verwendeten Instrumente (Injektionsnadeln, elektrochirurgische Messer mit unterschiedlichen Konfigurationen, h{\"a}mostatische Zangen) k{\"o}nnte wertvolle Informationen {\"u}ber den Fortschritt und die Verfahrensmerkmale der ESD liefern und eine automatische standardisierte Berichterstattung erm{\"o}glichen. Ziele: Ziel dieser Studie war die Entwicklung eines KI-Algorithmus zur Erkennung und Delineation von endoskopischen Instrumenten bei der ESD. Methodik: 17 ESD-Videos (9×rektal, 5×{\"o}sophageal, 3×gastrisch) wurden retrospektiv zusammengestellt. Auf 8530 Einzelbilder dieser Videos wurden durch 2 Studienmitarbeiter die folgenden Klassen eingezeichnet: Hakenmesser - Spitze, Hakenmesser - Katheter, Nadelmesser - Spitze und - Katheter, Injektionsnadel -Spitze und - Katheter sowie h{\"a}mostatische Zange - Spitze und - Katheter. Der annotierte Datensatz wurde zum Training eines DeepLabV3+-Deep-Learning-Algorithmus mit ConvNeXt-Backbone zur Erkennung und Abgrenzung der genannten Klassen verwendet. Die Evaluation erfolgte durch 5-fache interne Kreuzvalidierung. Ergebnis: Die Validierung auf Einzelpixelbasis ergab insgesamt einen F1-Score von 0,80, eine Sensitivit{\"a}t von 0,81 und eine Spezifit{\"a}t von 1,00. Es wurden F1-Scores von 1,00, 0,97, 0,80, 0,98, 0,85, 0,97, 0,80, 0,51 bzw. 0,85 f{\"u}r die Klassen Hakenmesser - Katheter und - Spitze, Nadelmesser - Katheter und - Spitze, Injektionsnadel - Katheter und - Spitze, h{\"a}mostatische Zange - Katheter und - Spitze gemessen. Schlussfolgerung: In dieser Studie wurden die wichtigsten endoskopischen Instrumente, die w{\"a}hrend der ESD verwendet werden, mit hoher Genauigkeit erkannt. Die geringere Leistung bei der h{\"a}mostatische Zange - Katheter kann auf die Unterrepr{\"a}sentation dieser Klassen in den Trainingsdaten zur{\"u}ckgef{\"u}hrt werden. Zuk{\"u}nftige Studien werden sich auf die Erweiterung der Instrumentenklassen sowie auf die Ausbalancierung der Trainingsdaten konzentrieren.}, language = {de} } @unpublished{GutbrodRauberWeberNunesetal., author = {Gutbrod, Max and Rauber, David and Weber Nunes, Danilo and Palm, Christoph}, title = {OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection}, doi = {10.48550/arXiv.2503.16247}, pages = {18}, abstract = {The growing reliance on Artificial Intelligence (AI) in critical domains such as healthcare demands robust mechanisms to ensure the trustworthiness of these systems, especially when faced with unexpected or anomalous inputs. This paper introduces the Open Medical Imaging Benchmarks for Out-Of-Distribution Detection (OpenMIBOOD), a comprehensive framework for evaluating out-of-distribution (OOD) detection methods specifically in medical imaging contexts. OpenMIBOOD includes three benchmarks from diverse medical domains, encompassing 14 datasets divided into covariate-shifted in-distribution, near-OOD, and far-OOD categories. We evaluate 24 post-hoc methods across these benchmarks, providing a standardized reference to advance the development and fair comparison of OOD detection methods. Results reveal that findings from broad-scale OOD benchmarks in natural image domains do not translate to medical applications, underscoring the critical need for such benchmarks in the medical field. By mitigating the risk of exposing AI models to inputs outside their training distribution, OpenMIBOOD aims to support the advancement of reliable and trustworthy AI systems in healthcare. The repository is available at this https URL.}, language = {en} }