Fraunhofer-Publica
The Fraunhofer-Publica has been successfully documenting the research results of the Fraunhofer-Gesellschaft for over 30 years. The platform enables the collaborative linking of research-relevant objects and disseminates within the international scientific community.
The Fraunhofer-Publica thus fulfils its responsibility to promote the transfer of knowledge and know-how to industry and society.
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Research outputs
As an application-oriented research organisation, Fraunhofer aims to conduct highly innovative and solution-oriented research - for the benefit of society and to strengthen the German and European economy.
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Projects
Fraunhofer is tackling the current challenges facing industry head on. By pooling their expertise and involving industrial partners at an early stage, the Fraunhofer Institutes involved in the projects aim to turn original scientific ideas into marketable products as quickly as possible.
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Researchers
Scientific achievement and practical relevance are not opposites - at Fraunhofer they are mutually dependent. Thanks to the close organisational links between Fraunhofer Institutes and universities, science at Fraunhofer is conducted at an internationally first-class level.
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Institutes
The Fraunhofer-Gesellschaft is the leading organisation for applied research in Europe. Institutes and research facilities work under its umbrella at various locations throughout Germany.
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PublicationMachine-based emotion-Assessment in waiting rooms - a feasibility and acceptance study( 2023)Background: Due to an aging society and changing health behaviors, emergency room crowding has become a major problem in western health care systems. Empowering patients and health care workers to assess necessary and relevant information is critical to streamline clinical workflows. Health kiosks, designed for services like self-check-in or (ideally contactless) health self-Assessment may be instrumental in solving this issue. Based on the collected data, automated workflows such as flagging critical patients, inducing specific diagnostics or early symptomatic treatment could be implemented. Objective: Using an AI-supported software, which visually analyzes and categorizes facial expressions, the emotional status of hemato-oncologic patients in a German oncology outpatient clinic was examined. Additionally a survey was conducted, evaluating the acceptance of such a self-Assessment solution. Results: 98% of the participants were not stressed by the real-Time emotion analysis. However, the current set of registered emotion categories was found to be only partially sufficient to adequately describe the emotional status of the patients. More importantly, 88% of the participants found such a system to be meaningful. Also, 84% of the participants agreed that such a self-Analysis could be of potential assistance. No relevant generation-or gender-specific differences could be observed. Discussion: Automated analysis of patients' emotional status can be a first step toward a more comprehensive assessment of the respective health status. Patients, in particular the elderly, approve to the vision and development of such a system. Next steps are a further improvement of the AI-based emotion recognition software with respect to more emotional states as well as the definition, inclusion and ideally contactless acquisition of physical biomarkers (as e.g. heart rate or respiratory rate) determining physical and mental well-being.
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PublicationTraining and education of young physicians and engineers in the field of endoscopy and laparoscopy - a review of Germany-wide joint cooperation and training possibilities( 2023)Introduction: Application-related (hands-on) training and instruction is an essential component in the advanced education of physicians in residency. To teach and train required endoscopic examinations and interventions in a standardized manner, courses are offered by various societies. Within these courses the assistant endoscopists learn and train how to use the technical equipment and how to perform the examinations and related interventions. Similarly, for graduate biomedical engineering students with a job-or research-related interest in the field of endoand laparoscopy, an early involvement and practical introduction to the instrumentation and techniques-if possible already during their studies-can generate a fundamental understanding of the technical requirements as well needs of the physicians. Objective: As various such (hands-on) training-courses for flexible and rigid endoscopy specifically addressing young physicians, surgeons as well as engineers are offered by numerous institutes, this contribution tries do provide a structured overview on/over these possibilities. Material: Known cross-institutional courses in the field of endo-and laparoscopy currently offered in Germany in cooperation of physicians and engineers are listed and briefly described. Results: A total of n = 4 crossinstitutional courses for the introduction and training in flexible and rigid endoscopy have been identified. Discussion: The cross-institutional courses for physicians and technicians in the field of endo-and laparoscopy presented serve to provide a better understanding of the subject matter and the field of either discipline. Questions that arise in the field of endoscopy from physicians can be addressed at an early stage and, if necessary, worked on with little effort. Ideas for new instruments and even new interventions can arise and be pursued through an intensive exchange between technicians and physicians. Collaborations are established on a local basis which will foster national activities in this field and which allow to develop competencies relevant for the industrial sector of medical engineering.
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PublicationDomain Transfer in Histopathology using Multi-ProtoNets with Interactive Prototype Adaptation( 2023)Few-shot learning addresses the problem of classification when little data or few labels are available. This is especially relevant in histopathology, where labeling must be carried out by highly trained medical experts. Prototypical Networks promise transferability to new domains by using a pre-Trained encoder and classifying by way of a prototypical representation of each class learned with few samples. We examine the applicability of this approach by attempting domain transfer from colon tissue (for training the encoder) to urothelial tissue. Furthermore, we address the problems arising from representing a class via a small amount of representatives (prototypes) by testing two different prototype calculation strategies. We compare the original "Prototype per Class"(PPC) approach to our "Prototype per Annotation"(PPA) method, which calculates one prototype for each example annotation made by the pathologist. We test the domain transfer capability of our approach on a dataset of 55 whole slide images (WSIs) containing six subtypes of urothelial carcinoma in two granularities: "Superclasses", which combines the tumorous subtypes into a single "tumor"class on top of a aggregated "healthy"and additional "necrosis"class, and "subtypes", which considers all eleven classes separately. We evaluate the classic PPC approach as well as our PPA approach on this data set. Our results show that the adaptation of the Prototypical Network from colon tissue to urothelial tissue was successful, yielding an F1 score of 0.91 for the "superclasses". Furthermore, the PPA approach performs very comparably to the PPC strategy. This makes it a viable alternative that places more value on the intent of the pathologist during annotation.
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PublicationLymph node metastases detection in Whole Slide Images using prototypical patterns and transformer-guided multiple instance learning( 2023)Background: The examination of lymph nodes (LNs) regarding metastases is vital for the staging of cancer patients, which is necessary for diagnosis and adequate treatment selection. Advancements in digital pathology, utilizing Whole-Slide Images (WSIs) and convolutional neural networks (CNNs), pose new opportunities to automate this procedure, thus reducing pathologists' workload while simultaneously increasing the accuracy in metastases detection. Objective: To address the task of LN-metastases detection, the use of weakly supervised transformers are applied for the analysis of WSIs. Methods Materials: As WSIs are too large to be processed as a whole, they are divided into non-overlapping patches, which are converted to feature vectors using a CNN network, pre-trained on HE-stained colon cancer resections. A subset of these patches serves as input for a transformer to predict if a LN contains a metastasis. Hence, selecting a representative subset is an important part of the pipeline. Hereby, a prototype based clustering is employed and different sampling strategies are tested. Finally, the chosen feature vectors are fed into a transformer-based multiple instance learning (MIL) architecture, classifying the LNs into healthy/negative (that is, containing no metastases), or metastatic/positive (that is, containing metastases). The proposed model is trained only on the Camelyon16 training data (LNs from breast cancer patients), and evaluated on the Camelyon16 test set. Results: The trained model achieves accuracies of up to 92.3% on the test data (from breast LNs). While the model struggles with smaller metastases, high specificities of up to 96.9% can be accomplished. Additionally, the model is evaluated on LNs from a different primary tumor (colon), where accuracies between 62.3% and 95.9% could be obtained. Conclusion: The investigated transformer-model performs very good on LN data from the public LN breast data, but the domain transfer to LNs from the colon needs more research.
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PublicationA smallest computable entanglement monotone( 2022)The Rains relative entropy of a bipartite quantum state is the tightest known upper bound on its distillable entanglement - which has a crisp physical interpretation of entanglement as a resource - and it is efficiently computable by convex programming. It has not been known to be a selective entanglement monotone in its own right. In this work, we strengthen the interpretation of the Rains relative entropy by showing that it is monotone under the action of selective operations that completely preserve the positivity of the partial transpose, reasonably quantifying entanglement. That is, we prove that Rains relative entropy of an ensemble generated by such an operation does not exceed the Rains relative entropy of the initial state in expectation, giving rise to the smallest, most conservative known computable selective entanglement monotone. Additionally, we show that this is true not only for the original Rains relative entropy, but also for Rains relative entropies derived from various Rényi relative entropies. As an application of these findings, we prove, in both the non-asymptotic and asymptotic settings, that the probabilistic approximate distillable entanglement of a state is bounded from above by various Rains relative entropies.Full version available at https://arxiv.org/abs/2201.00835
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