| On the objectivity, reliability, and validity of deep learning enabled bioimage analyses D Segebarth, M Griebel, N Stein, CR von Collenberg, C Martin, D Fiedler, ... Elife 9, e59780, 2020 | 31 | 2020 |
| Predicting Fraudulent initial Coin Offerings using Information Extracted from Whitepapers. A Dürr, M Griebel, G Welsch, F Thiesse ECIS, 2020 | 17 | 2020 |
| A hidden Markov model for distinguishing between RFID-tagged objects in adjacent areas M Hauser, M Griebel, F Thiesse 2017 IEEE international conference on RFID (RFID), 167-173, 2017 | 17 | 2017 |
| Augmented creativity: Leveraging artificial intelligence for idea generation in the creative sphere M Griebel, C Flath, S Friesike Twenty-Eighth European Conference on Information Systems (ECIS2020), 2020 | 12 | 2020 |
| Applied image recognition: guidelines for using deep learning models in practice M Griebel, A Dürr, N Stein 14th International Conference on Wirtschaftsinformatik, February 24-27, 2019 …, 2019 | 10 | 2019 |
| Augmented Intelligence for Quality Control of Manual Assembly Processes using Industrial Wearable Systems. A Krenzer, N Stein, M Griebel, C Flath Fortieth International Conference on Information Systems (ICIS), Munich 2019, 2019 | 9 | 2019 |
| Empowering smarter fitting rooms with RFID data analytics M Hauser, M Griebel, J Hanke, F Thiesse 13th International Conference on Wirtschaftsinformatik, February 12-15, 2017 …, 2017 | 8 | 2017 |
| DeepFLaSh, a deep learning pipeline for segmentation of fluorescent labels in microscopy images D Segebarth, M Griebel, A Duerr, CR von Collenberg, C Martin, D Fiedler bioRxiv, 473199, 2018 | 6 | 2018 |
| Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2 M Griebel, D Segebarth, N Stein, N Schukraft, P Tovote, R Blum, CM Flath arXiv preprint arXiv:2111.06693, 2021 | 4 | 2021 |
| A picture is worth more than a thousand purchases: designing an image-based fashion curation system M Griebel, G Welsch, T Greif, C Flath Twenty-Seventh European Conference on Information Systems (ECIS2019 …, 2019 | 4 | 2019 |
| ADR for Big-Data IT Artifact Development: An Escalation Management Example F Oberdorf, N Stein, N Walk, M Griebel, C Flath | 3 | 2020 |
| Deep learning-enabled segmentation of ambiguous bioimages with deepflash2 M Griebel, D Segebarth, N Stein, N Schukraft, P Tovote, R Blum, CM Flath Nature Communications 14 (1), 1679, 2023 | 2 | 2023 |
| A deep learning model trained on only eight whole-slide images accurately segments tumors: wise data use versus big data T Perennec, R Bourgade, S Henno, C Sagan, C Toquet, N Rioux-Leclercq, ... BioRxiv, 2022.02. 07.478680, 2022 | 1 | 2022 |
| Applied Deep Learning: from Data to Deployment M Griebel University of Würzburg, 2022 | | 2022 |
| Deep learning can increase the reliability, objectivity, reproducibility and transparency in image data analysis D Segebarth, M Griebel, A Duerr, RC von Collenberg, C Martin, ... JOURNAL OF NEURAL TRANSMISSION 126 (11), 1536-1537, 2019 | | 2019 |
Christoph M. FlathJulius-Maximilians-Universität WürzburgVerified email at uni-wuerzburg.de
