Dominik Müller

1.1k total citations · 1 hit paper
16 papers, 540 citations indexed

About

Dominik Müller is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Dominik Müller has authored 16 papers receiving a total of 540 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Dominik Müller's work include Radiomics and Machine Learning in Medical Imaging (10 papers), AI in cancer detection (6 papers) and Brain Tumor Detection and Classification (4 papers). Dominik Müller is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (10 papers), AI in cancer detection (6 papers) and Brain Tumor Detection and Classification (4 papers). Dominik Müller collaborates with scholars based in Germany, Spain and United States. Dominik Müller's co-authors include Frank Krämer, Iñaki Soto‐Rey, Florian Auer, Philip Meyer, Santiago Cepeda, Ignacio Arrese, Rosario Sarabia, Ramón Torné, Sergio García-García and Ludwig Christian Hinske and has published in prestigious journals such as IEEE Access, BMC Research Notes and Diagnostics.

In The Last Decade

Dominik Müller

11 papers receiving 530 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Towards a guideline for evaluation metrics in medical image segmentation

2022 281 citations Dominik Müller, Iñaki Soto‐Rey et al. BMC Research Notes profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Dominik Müller Germany	6	270	173	141	80	63	16	540
Iñaki Soto‐Rey Germany	7	219 0.8×	143 0.8×	107 0.8×	63 0.8×	53 0.8×	28	521
Mahboubeh Jannesari Germany	3	245 0.9×	234 1.4×	108 0.8×	79 1.0×	55 0.9×	7	510
Tahir Mahmood South Korea	13	342 1.3×	232 1.3×	192 1.4×	77 1.0×	55 0.9×	30	620
Dexing Kong China	14	381 1.4×	211 1.2×	133 0.9×	82 1.0×	53 0.8×	54	664
D. R. Sarvamangala India	3	207 0.8×	194 1.1×	133 0.9×	70 0.9×	87 1.4×	5	541
Phillip Chlap Australia	10	359 1.3×	198 1.1×	182 1.3×	117 1.5×	79 1.3×	30	792
Dzhoshkun I. Shakir United Kingdom	9	221 0.8×	131 0.8×	160 1.1×	105 1.3×	60 1.0×	22	532
Zhennan Yan United States	13	281 1.0×	228 1.3×	205 1.5×	131 1.6×	41 0.7×	37	616
Kim Eun Hee Germany	5	201 0.7×	180 1.0×	82 0.6×	77 1.0×	42 0.7×	25	496

Countries citing papers authored by Dominik Müller

Since Specialization

Citations

This map shows the geographic impact of Dominik Müller's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dominik Müller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dominik Müller more than expected).

Fields of papers citing papers by Dominik Müller

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dominik Müller. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dominik Müller. The network helps show where Dominik Müller may publish in the future.

Co-authorship network of co-authors of Dominik Müller

This figure shows the co-authorship network connecting the top 25 collaborators of Dominik Müller. A scholar is included among the top collaborators of Dominik Müller based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dominik Müller. Dominik Müller is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

Meyer, Philip, Dominik Müller, Anna Muzalyova, et al.. (2025). Do explainable AI (XAI) methods improve the acceptance of AI in clinical practice? An evaluation of XAI methods on Gleason grading. The Journal of Pathology Clinical Research. 11(2). e70023–e70023.

Müller, Dominik, et al.. (2025). Automatic Segmentation of Histopathological Glioblastoma Whole-Slide Images Utilizing MONAI. Studies in health technology and informatics. 327. 88–92.

Cepeda, Santiago, et al.. (2024). Predicting Overall Survival of Glioblastoma Patients Using Deep Learning Classification Based on MRIs. Studies in health technology and informatics. 317. 356–365. 3 indexed citations

Schmid, Verena, Philip Meyer, Florian Auer, et al.. (2023). MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data. Diagnostics. 13(16). 2618–2618.

García-García, Sergio, Santiago Cepeda, Dominik Müller, et al.. (2023). Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan. Brain Sciences. 14(1). 10–10. 5 indexed citations

Auer, Florian, et al.. (2023). The RCX Extension Hub: A Resource for Implementations Extending the R Adaption of the Cytoscape Exchange Format. Studies in health technology and informatics. 302. 1075–1076.

Kaiser, Lena, et al.. (2023). Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data. Nuklearmedizin - NuclearMedicine. 62(6). 389–398. 1 indexed citations

Müller, Dominik, Iñaki Soto‐Rey, & Frank Krämer. (2022). Towards a guideline for evaluation metrics in medical image segmentation. BMC Research Notes. 15(1). 210–210. 281 indexed citations breakdown →

Müller, Dominik, et al.. (2022). Towards a Guideline for Evaluation Metrics in Medical Image Segmentation. Zenodo (CERN European Organization for Nuclear Research). 10 indexed citations

10.

Auer, Florian, et al.. (2022). Adaptation of HL7 FHIR for the Exchange of Patients’ Gene Expression Profiles. Studies in health technology and informatics. 295. 332–335. 1 indexed citations

11.

Müller, Dominik, Iñaki Soto‐Rey, & Frank Krämer. (2022). An Analysis on Ensemble Learning Optimized Medical Image Classification With Deep Convolutional Neural Networks. IEEE Access. 10. 66467–66480. 59 indexed citations

12.

Auer, Florian, et al.. (2022). Perspective on Code Submission and Automated Evaluation Platforms for University Teaching. Studies in health technology and informatics. 290. 912–916.

13.

Müller, Dominik, et al.. (2022). MISeval: A Metric Library for Medical Image Segmentation Evaluation. Studies in health technology and informatics. 294. 33–37. 14 indexed citations

14.

Müller, Dominik & Frank Krämer. (2021). MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning. BMC Medical Imaging. 21(1). 12–12. 91 indexed citations

15.

Müller, Dominik, Iñaki Soto‐Rey, & Frank Krämer. (2021). Robust chest CT image segmentation of COVID-19 lung infection based on limited data. Informatics in Medicine Unlocked. 25. 100681–100681. 71 indexed citations

16.

Müller, Dominik, Iñaki Soto‐Rey, & Frank Krämer. (2021). Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models. Zenodo (CERN European Organization for Nuclear Research). 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact