@article{KrenzerMakowskiHekaloetal.2022,
  author    = {Krenzer, Adrian and Makowski, Kevin and Hekalo, Amar and Fitting, Daniel and Troya, Joel and Zoller, Wolfram G. and Hann, Alexander and Puppe, Frank},
  title     = {Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists},
  series = {BioMedical Engineering OnLine},
  volume    = {21},
  journal   = {BioMedical Engineering OnLine},
  number    = {1},
  doi       = {10.1186/s12938-022-01001-x},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300231},
  year      = {2022},
  abstract  = {Background Machine learning, especially deep learning, is becoming more and more relevant in research and development in the medical domain. For all the supervised deep learning applications, data is the most critical factor in securing successful implementation and sustaining the progress of the machine learning model. Especially gastroenterological data, which often involves endoscopic videos, are cumbersome to annotate. Domain experts are needed to interpret and annotate the videos. To support those domain experts, we generated a framework. With this framework, instead of annotating every frame in the video sequence, experts are just performing key annotations at the beginning and the end of sequences with pathologies, e.g., visible polyps. Subsequently, non-expert annotators supported by machine learning add the missing annotations for the frames in-between. Methods In our framework, an expert reviews the video and annotates a few video frames to verify the object's annotations for the non-expert. In a second step, a non-expert has visual confirmation of the given object and can annotate all following and preceding frames with AI assistance. After the expert has finished, relevant frames will be selected and passed on to an AI model. This information allows the AI model to detect and mark the desired object on all following and preceding frames with an annotation. Therefore, the non-expert can adjust and modify the AI predictions and export the results, which can then be used to train the AI model. Results Using this framework, we were able to reduce workload of domain experts on average by a factor of 20 on our data. This is primarily due to the structure of the framework, which is designed to minimize the workload of the domain expert. Pairing this framework with a state-of-the-art semi-automated AI model enhances the annotation speed further. Through a prospective study with 10 participants, we show that semi-automated annotation using our tool doubles the annotation speed of non-expert annotators compared to a well-known state-of-the-art annotation tool. Conclusion In summary, we introduce a framework for fast expert annotation for gastroenterologists, which reduces the workload of the domain expert considerably while maintaining a very high annotation quality. The framework incorporates a semi-automated annotation system utilizing trained object detection models. The software and framework are open-source.},
  language  = {en}
}
@article{KrenzerHeilFittingetal.,
  author    = {Krenzer, Adrian and Heil, Stefan and Fitting, Daniel and Matti, Safa and Zoller, Wolfram G. and Hann, Alexander and Puppe, Frank},
  title     = {Automated classification of polyps using deep learning architectures and few-shot learning},
  series = {BMC Medical Imaging},
  volume    = {23},
  journal   = {BMC Medical Imaging},
  doi       = {10.1186/s12880-023-01007-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357465},
  abstract  = {Background Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification. Methods We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database. Results For the Paris classification, we achieve an accuracy of 89.35 \%, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 \% and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations. Conclusion Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.},
  language  = {en}
}
@phdthesis{Krenzer2023,
  author    = {Krenzer, Adrian},
  title     = {Machine learning to support physicians in endoscopic examinations with a focus on automatic polyp detection in images and videos},
  doi       = {10.25972/OPUS-31911},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-319119},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Deep learning enables enormous progress in many computer vision-related tasks. Artificial Intel- ligence (AI) steadily yields new state-of-the-art results in the field of detection and classification. Thereby AI performance equals or exceeds human performance. Those achievements impacted many domains, including medical applications. One particular field of medical applications is gastroenterology. In gastroenterology, machine learning algorithms are used to assist examiners during interventions. One of the most critical concerns for gastroenterologists is the development of Colorectal Cancer (CRC), which is one of the leading causes of cancer-related deaths worldwide. Detecting polyps in screening colonoscopies is the essential procedure to prevent CRC. Thereby, the gastroenterologist uses an endoscope to screen the whole colon to find polyps during a colonoscopy. Polyps are mucosal growths that can vary in severity. This thesis supports gastroenterologists in their examinations with automated detection and clas- sification systems for polyps. The main contribution is a real-time polyp detection system. This system is ready to be installed in any gastroenterology practice worldwide using open-source soft- ware. The system achieves state-of-the-art detection results and is currently evaluated in a clinical trial in four different centers in Germany. The thesis presents two additional key contributions: One is a polyp detection system with ex- tended vision tested in an animal trial. Polyps often hide behind folds or in uninvestigated areas. Therefore, the polyp detection system with extended vision uses an endoscope assisted by two additional cameras to see behind those folds. If a polyp is detected, the endoscopist receives a vi- sual signal. While the detection system handles the additional two camera inputs, the endoscopist focuses on the main camera as usual. The second one are two polyp classification models, one for the classification based on shape (Paris) and the other on surface and texture (NBI International Colorectal Endoscopic (NICE) classification). Both classifications help the endoscopist with the treatment of and the decisions about the detected polyp. The key algorithms of the thesis achieve state-of-the-art performance. Outstandingly, the polyp detection system tested on a highly demanding video data set shows an F1 score of 90.25 \% while working in real-time. The results exceed all real-time systems in the literature. Furthermore, the first preliminary results of the clinical trial of the polyp detection system suggest a high Adenoma Detection Rate (ADR). In the preliminary study, all polyps were detected by the polyp detection system, and the system achieved a high usability score of 96.3 (max 100). The Paris classification model achieved an F1 score of 89.35 \% which is state-of-the-art. The NICE classification model achieved an F1 score of 81.13 \%. Furthermore, a large data set for polyp detection and classification was created during this thesis. Therefore a fast and robust annotation system called Fast Colonoscopy Annotation Tool (FastCAT) was developed. The system simplifies the annotation process for gastroenterologists. Thereby the i gastroenterologists only annotate key parts of the endoscopic video. Afterward, those video parts are pre-labeled by a polyp detection AI to speed up the process. After the AI has pre-labeled the frames, non-experts correct and finish the annotation. This annotation process is fast and ensures high quality. FastCAT reduces the overall workload of the gastroenterologist on average by a factor of 20 compared to an open-source state-of-art annotation tool.},
  subject      = {Deep Learning},
  language  = {en}
}
@article{KraftReichertPryss2021,
  author    = {Kraft, Robin and Reichert, Manfred and Pryss, R{\"u}diger},
  title     = {Towards the interpretation of sound measurements from smartphones collected with mobile crowdsensing in the healthcare domain: an experiment with Android devices},
  series = {Sensors},
  volume    = {22},
  journal   = {Sensors},
  number    = {1},
  issn      = {1424-8220},
  doi       = {10.3390/s22010170},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252246},
  year      = {2021},
  abstract  = {The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users' individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.},
  language  = {en}
}
@article{KraftBirkReichertetal.2020,
  author    = {Kraft, Robin and Birk, Ferdinand and Reichert, Manfred and Deshpande, Aniruddha and Schlee, Winfried and Langguth, Berthold and Baumeister, Harald and Probst, Thomas and Spiliopoulou, Myra and Pryss, R{\"u}diger},
  title     = {Efficient processing of geospatial mHealth data using a scalable crowdsensing platform},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {12},
  issn      = {1424-8220},
  doi       = {10.3390/s20123456},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207826},
  year      = {2020},
  abstract  = {Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.},
  language  = {en}
}
@techreport{KounevBrosigHuber2014,
  author    = {Kounev, Samuel and Brosig, Fabian and Huber, Nikolaus},
  title     = {The Descartes Modeling Language},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-104887},
  pages     = {91},
  year      = {2014},
  abstract  = {This technical report introduces the Descartes Modeling Language (DML), a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. DML is designed to serve as a basis for self-aware resource management during operation ensuring that system QoS requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible.},
  subject      = {Ressourcenmanagement},
  language  = {en}
}
@phdthesis{Kosub2001,
  author    = {Kosub, Sven},
  title     = {Complexity and Partitions},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-2808},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2001},
  abstract  = {Computational complexity theory usually investigates the complexity of sets, i.e., the complexity of partitions into two parts. But often it is more appropriate to represent natural problems by partitions into more than two parts. A particularly interesting class of such problems consists of classification problems for relations. For instance, a binary relation R typically defines a partitioning of the set of all pairs (x,y) into four parts, classifiable according to the cases where R(x,y) and R(y,x) hold, only R(x,y) or only R(y,x) holds or even neither R(x,y) nor R(y,x) is true. By means of concrete classification problems such as Graph Embedding or Entailment (for propositional logic), this thesis systematically develops tools, in shape of the boolean hierarchy of NP-partitions and its refinements, for the qualitative analysis of the complexity of partitions generated by NP-relations. The Boolean hierarchy of NP-partitions is introduced as a generalization of the well-known and well-studied Boolean hierarchy (of sets) over NP. Whereas the latter hierarchy has a very simple structure, the situation is much more complicated for the case of partitions into at least three parts. To get an idea of this hierarchy, alternative descriptions of the partition classes are given in terms of finite, labeled lattices. Based on these characterizations the Embedding Conjecture is established providing the complete information on the structure of the hierarchy. This conjecture is supported by several results. A natural extension of the Boolean hierarchy of NP-partitions emerges from the lattice-characterization of its classes by considering partition classes generated by finite, labeled posets. It turns out that all significant ideas translate from the case of lattices. The induced refined Boolean hierarchy of NP-partitions enables us more accuratly capturing the complexity of certain relations (such as Graph Embedding) and a description of projectively closed partition classes.},
  subject      = {Partition <Mengenlehre>},
  language  = {en}
}
@article{KoopmannStubbemannKapaetal.2021,
  author    = {Koopmann, Tobias and Stubbemann, Maximilian and Kapa, Matthias and Paris, Michael and Buenstorf, Guido and Hanika, Tom and Hotho, Andreas and J{\"a}schke, Robert and Stumme, Gerd},
  title     = {Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research},
  series = {Scientometrics},
  volume    = {126},
  journal   = {Scientometrics},
  number    = {12},
  issn      = {1588-2861},
  doi       = {10.1007/s11192-021-03922-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-269831},
  pages     = {9847-9868},
  year      = {2021},
  abstract  = {Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.},
  language  = {en}
}
@article{KlemzRote2022,
  author    = {Klemz, Boris and Rote, G{\"u}nter},
  title     = {Linear-Time Algorithms for Maximum-Weight Induced Matchings and Minimum Chain Covers in Convex Bipartite Graphs},
  series = {Algorithmica},
  volume    = {84},
  journal   = {Algorithmica},
  number    = {4},
  issn      = {1432-0541},
  doi       = {10.1007/s00453-021-00904-w},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-267876},
  pages     = {1064-1080},
  year      = {2022},
  abstract  = {A bipartite graph G=(U,V,E) is convex if the vertices in V can be linearly ordered such that for each vertex u∈U, the neighbors of u are consecutive in the ordering of V. An induced matching H of G is a matching for which no edge of E connects endpoints of two different edges of H. We show that in a convex bipartite graph with n vertices and m weighted edges, an induced matching of maximum total weight can be computed in O(n+m) time. An unweighted convex bipartite graph has a representation of size O(n) that records for each vertex u∈U the first and last neighbor in the ordering of V. Given such a compact representation, we compute an induced matching of maximum cardinality in O(n) time. In convex bipartite graphs, maximum-cardinality induced matchings are dual to minimum chain covers. A chain cover is a covering of the edge set by chain subgraphs, that is, subgraphs that do not contain induced matchings of more than one edge. Given a compact representation, we compute a representation of a minimum chain cover in O(n) time. If no compact representation is given, the cover can be computed in O(n+m) time. All of our algorithms achieve optimal linear running time for the respective problem and model, and they improve and generalize the previous results in several ways: The best algorithms for the unweighted problem versions had a running time of O(n\(^{2}\)) (Brandst{\"a}dt et al. in Theor. Comput. Sci. 381(1-3):260-265, 2007. https://doi.org/10.1016/j.tcs.2007.04.006). The weighted case has not been considered before.},
  language  = {en}
}
@phdthesis{Klein2014,
  author    = {Klein, Dominik Werner},
  title     = {Design and Evaluation of Components for Future Internet Architectures},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-9313},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-93134},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Die derzeitige Internetarchitektur wurde nicht in einem geplanten Prozess konzipiert und entwickelt, sondern hat vielmehr eine evolutionsartige Entwicklung hinter sich. Ausl{\"o}ser f{\"u}r die jeweiligen Evolutionsschritte waren dabei meist aufstrebende Anwendungen, welche neue Anforderungen an die zugrundeliegende Netzarchitektur gestellt haben. Um diese Anforderungen zu erf{\"u}llen, wurden h{\"a}ufig neuartige Dienste oder Protokolle spezifiziert und in die bestehende Architektur integriert. Dieser Prozess ist jedoch meist mit hohem Aufwand verbunden und daher sehr tr{\"a}ge, was die Entwicklung und Verbreitung innovativer Dienste beeintr{\"a}chtigt. Derzeitig diskutierte Konzepte wie Software-Defined Networking (SDN) oder Netzvirtualisierung (NV) werden als eine M{\"o}glichkeit angesehen, die Altlasten der bestehenden Internetarchitektur zu l{\"o}sen. Beiden Konzepten gemein ist die Idee, logische Netze {\"u}ber dem physikalischen Substrat zu betreiben. Diese logischen Netze sind hochdynamisch und k{\"o}nnen so flexibel an die Anforderungen der jeweiligen Anwendungen angepasst werden. Insbesondere erlaubt das Konzept der Virtualisierung intelligentere Netzknoten, was innovative neue Anwendungsf{\"a}lle erm{\"o}glicht. Ein h{\"a}ufig in diesem Zusammenhang diskutierter Anwendungsfall ist die Mobilit{\"a}t sowohl von Endger{\"a}ten als auch von Diensten an sich. Die Mobilit{\"a}t der Dienste wird hierbei ausgenutzt, um die Zugriffsverz{\"o}gerung oder die belegten Ressourcen im Netz zu reduzieren, indem die Dienste zum Beispiel in f{\"u}r den Nutzer geographisch nahe Datenzentren migriert werden. Neben den reinen Mechanismen bez{\"u}glich Dienst- und Endger{\"a}temobilit{\"a}t sind in diesem Zusammenhang auch geeignete {\"U}berwachungsl{\"o}sungen relevant, welche die vom Nutzer wahrgenommene Dienstg{\"u}te bewerten k{\"o}nnen. Diese L{\"o}sungen liefern wichtige Entscheidungshilfen f{\"u}r die Migration oder {\"u}berwachen m{\"o}gliche Effekte der Migration auf die erfahrene Dienstg{\"u}te beim Nutzer. Im Falle von Video Streaming erm{\"o}glicht ein solcher Anwendungsfall die flexible Anpassung der Streaming Topologie f{\"u}r mobile Nutzer, um so die Videoqualit{\"a}t unabh{\"a}ngig vom Zugangsnetz aufrechterhalten zu k{\"o}nnen. Im Rahmen dieser Doktorarbeit wird der beschriebene Anwendungsfall am Beispiel einer Video Streaming Anwendung n{\"a}her analysiert und auftretende Herausforderungen werden diskutiert. Des Weiteren werden L{\"o}sungsans{\"a}tze vorgestellt und bez{\"u}glich ihrer Effizienz ausgewertet. Im Detail besch{\"a}ftigt sich die Arbeit mit der Leistungsanalyse von Mechanismen f{\"u}r die Dienstmobilit{\"a}t und entwickelt eine Architektur zur Optimierung der Dienstmobilit{\"a}t. Im Bereich Endger{\"a}temobilit{\"a}t werden Verbesserungen entwickelt, welche die Latenz zwischen Endger{\"a}t und Dienst reduzieren oder die Konnektivit{\"a}t unabh{\"a}ngig vom Zugangsnetz gew{\"a}hrleisten. Im letzten Teilbereich wird eine L{\"o}sung zur {\"U}berwachung der Videoqualit{\"a}t im Netz entwickelt und bez{\"u}glich ihrer Genauigkeit analysiert.},
  subject      = {Leistungsbewertung},
  language  = {en}
}