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This thesis will be carried out in conjunction with the CellFace project, which investigates the potential of computer vision and machine learning techniques for the task of automated blood cell diagnosis. [mehr]

Topic The perception of Machine Learning (ML) based decision support systems as “black boxes” is a significant barrier to adoption. This is especially the case in high-stakes domains like medicine, where such systems need to earn a high degree of trust before their usage is approved. Recent publications in the field of explainable AI (XAI) suggest that relaying a system’s... [mehr]

Topic Latest advancements in artificial intelligence show great promise for medical imaging. Automated screening and diagnosis tools based on machine learning techniques could help make medical tests faster, cheaper, and more readily available to patients. Large medical imaging datasets and successful machine learning architectures from the field of computer vision already... [mehr]

Topic A common obstacle for the successful adoption of machine learning (ML) techniques in real world applications is the problem of out-of-distribution data. In realistic contexts, ML models are likely to be confronted with data that differs significantly from the data they were trained on. This could for example be due to label noise, outliers, or a shift or drift in the... [mehr]

Abschlussseminar am Freitag, den 11.09.2020 um 09:00 Uhr via Zoom   [1] Anhand erhobener Messdaten aus Testfahrzeugen Schlüsse ziehen, inwiefern ein neues PRE-SAFE(R) Sicherheitssystem für die Folgenminimierung von Unfällen am Stauende Sinn ergibt IP Vortragender: Florian Bürger  Betreuer: Mercedes-Benz / Prof. Diepold   [2] Development of an application to guide... [mehr]