| Towards a guideline for evaluation metrics in medical image segmentation D Müller, I Soto-Rey, F Kramer BMC Research Notes 15 (1), 210, 2022 | 154 | 2022 |
| MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning D Müller, F Kramer BMC medical imaging 21, 1-11, 2021 | 131 | 2021 |
| Robust chest CT image segmentation of COVID-19 lung infection based on limited data D Müller, I Soto-Rey, F Kramer Informatics in medicine unlocked 25, 100681, 2021 | 124* | 2021 |
| An analysis on ensemble learning optimized medical image classification with deep convolutional neural networks D Müller, I Soto-Rey, F Kramer Ieee Access 10, 66467-66480, 2022 | 35 | 2022 |
| Biomedical image analysis competitions: The state of current participation practice M Eisenmann, A Reinke, V Weru, MD Tizabi, F Isensee, TJ Adler, ... arXiv preprint arXiv:2212.08568, 2022 | 23 | 2022 |
| Multi-disease detection in retinal imaging based on ensembling heterogeneous deep learning models D Müller, I Soto-Rey, F Kramer German Medical Data Sciences 2021: Digital Medicine: Recognize–Understand …, 2021 | 22 | 2021 |
| MISeval: A Metric Library for Medical Image Segmentation Evaluation. D Müller, D Hartmann, P Meyer, F Auer, IS Rey, F Kramer MIE, 33-37, 2022 | 18 | 2022 |
| Assessing the role of random forests in medical image segmentation D Hartmann, D Müller, I Soto-Rey, F Kramer arXiv preprint arXiv:2103.16492, 2021 | 8 | 2021 |
| Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework P Schneider, D Müller, F Kramer arXiv preprint arXiv:2110.01017, 2021 | 6 | 2021 |
| COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning P Meyer, D Müller, I Soto-Rey, F Kramer Studies in Health Technology and Informatics 281 (Public Health and …, 2021 | 6 | 2021 |
| AUCMEDI: Von der Insellösung zur einheitlichen und automatischen Klassifizierung von Medizinischen Bildern D Müller, D Hartmann, I Soto-Rey, F Kramer BVM Workshop, 253-253, 2023 | 2 | 2023 |
| Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan S García-García, S Cepeda, D Müller, A Mosteiro, R Torné, S Agudo, ... Brain Sciences 14 (1), 10, 2023 | 1 | 2023 |
| MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data D Hartmann, V Schmid, P Meyer, F Auer, I Soto-Rey, D Müller, F Kramer Diagnostics 13 (16), 2618, 2023 | 1 | 2023 |
| Towards automated COVID-19 presence and severity classification D Müller, N Schröter, S Mertes, F Hellmann, M Elia, W Reif, B Bauer, ... arXiv preprint arXiv:2305.08660, 2023 | 1 | 2023 |
| Standardized Medical Image Classification across Medical Disciplines S Mayer, D Müller, F Kramer arXiv preprint arXiv:2210.11091, 2022 | 1 | 2022 |
| Adaptation of HL7 FHIR for the Exchange of Patients' Gene Expression Profiles. F Auer, Z Abdykalykova, D Müller, F Kramer ICIMTH, 332-335, 2022 | 1 | 2022 |
| DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks D Müller, P Meyer, L Rentschler, R Manz, J Bäcker, S Cramer, ... arXiv preprint arXiv:2403.16678, 2024 | | 2024 |
| Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer D Müller, P Meyer, L Rentschler, R Manz, D Hieber, J Bäcker, S Cramer, ... arXiv preprint arXiv:2403.16695, 2024 | | 2024 |
| Enhancing interoperability and harmonisation of nuclear medicine image data and associated clinical data T Fuchs, L Kaiser, D Müller, L Papp, R Fischer, J Tran-Gia Nuklearmedizin-NuclearMedicine, 2023 | | 2023 |
| Neurological outcome prediction in patients with subarachnoid hemorrhage using a model based on initial CT scan, clinical data and neural networks S García, S Cepeda, D Müller, A Mosteiro-Cadaval, R Torne, S Agudo, ... | | 2023 |
Frank KramerIT Infrastructure for Translational Medical Research, University of AugsburgVerified email at informatik.uni-augsburg.de
