TY  - JOUR
A1  - Griebel, Matthias
A1  - Segebarth, Dennis
A1  - Stein, Nikolai
A1  - Schukraft, Nina
A1  - Tovote, Philip
A1  - Blum, Robert
A1  - Flath, Christoph M.
T1  - Deep learning-enabled segmentation of ambiguous bioimages with deepflash2
JF  - Nature Communications
N2  - Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the training, evaluation, and application of deep learning models on ambiguous data. The tool’s training and evaluation pipeline uses multiple expert annotations and deep model ensembles to achieve accurate results. The application pipeline supports various use-cases for expert annotations and includes a quality assurance mechanism in the form of uncertainty measures. Benchmarked against other tools, deepflash2 offers both high predictive accuracy and efficient computational resource usage. The tool is built upon established deep learning libraries and enables sharing of trained model ensembles with the research community. deepflash2 aims to simplify the integration of deep learning into bioimage analysis projects while improving accuracy and reliability.
KW  - machine learning
KW  - microscopy
KW  - quality control
KW  - software
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-357286
VL  - 14
ER  - 
TY  - JOUR
A1  - Oberdorf, Felix
A1  - Schaschek, Myriam
A1  - Weinzierl, Sven
A1  - Stein, Nikolai
A1  - Matzner, Martin
A1  - Flath, Christoph M.
T1  - Predictive end-to-end enterprise process network monitoring
JF  - Business & Information Systems Engineering
N2  - Ever-growing data availability combined with rapid progress in analytics has laid the foundation for the emergence of business process analytics. Organizations strive to leverage predictive process analytics to obtain insights. However, current implementations are designed to deal with homogeneous data. Consequently, there is limited practical use in an organization with heterogeneous data sources. The paper proposes a method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks to overcome this limitation. A case study performed with a medium-sized German manufacturing company highlights the method’s utility for organizations.
KW  - predictive process analytics
KW  - predictive process monitoring
KW  - deep learning
KW  - machine learning
KW  - neural network
KW  - business process anagement
KW  - process mining
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323814
SN  - 2363-7005
VL  - 65
IS  - 1
ER  -