Tom Brosch

1.6k total citations · 1 hit paper
20 papers, 699 citations indexed

About

Tom Brosch is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Epidemiology. According to data from OpenAlex, Tom Brosch has authored 20 papers receiving a total of 699 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 10 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Epidemiology. Recurrent topics in Tom Brosch's work include Medical Image Segmentation Techniques (7 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and AI in cancer detection (4 papers). Tom Brosch is often cited by papers focused on Medical Image Segmentation Techniques (7 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and AI in cancer detection (4 papers). Tom Brosch collaborates with scholars based in Germany, Canada and United States. Tom Brosch's co-authors include Roger Tam, Youngjin Yoo, David K.B. Li, Anthony Traboulsee, Lisa Tang, Axel Saalbach, Alexander Rauscher, Shannon Kolind, Irene M. Vavasour and Alex L. MacKay and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Medical Imaging and Neural Computation.

In The Last Decade

Tom Brosch

19 papers receiving 674 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.

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation

2016 306 citations Tom Brosch, Lisa Tang et al. IEEE Transactions on Medical Imaging profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tom Brosch Germany	10	288	278	215	169	115	20	699
Youngjin Yoo Canada	13	388 1.3×	228 0.8×	203 0.9×	155 0.9×	101 0.9×	30	748
Alfiia Galimzianova United States	8	438 1.5×	379 1.4×	263 1.2×	354 2.1×	157 1.4×	15	1.0k
Eloy Roura Spain	11	208 0.7×	297 1.1×	101 0.5×	226 1.3×	73 0.6×	18	666
Sandra González-Villà Spain	8	190 0.7×	278 1.0×	116 0.5×	222 1.3×	69 0.6×	11	551
Andrew J. Asman United States	15	334 1.2×	371 1.3×	89 0.4×	89 0.5×	142 1.2×	35	754
Žiga Špiclin Slovenia	14	259 0.9×	298 1.1×	63 0.3×	83 0.5×	134 1.2×	53	714
Amod Jog United States	15	227 0.8×	341 1.2×	87 0.4×	74 0.4×	143 1.2×	32	698
Dana Cobzaş Canada	17	243 0.8×	347 1.2×	54 0.3×	146 0.9×	77 0.7×	54	943
Darko Zikic United States	18	484 1.7×	777 2.8×	188 0.9×	345 2.0×	263 2.3×	38	1.3k
Andrea U. J. Mewes United States	8	278 1.0×	417 1.5×	150 0.7×	70 0.4×	118 1.0×	8	878

Countries citing papers authored by Tom Brosch

Since Specialization

Citations

This map shows the geographic impact of Tom Brosch'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 Tom Brosch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Brosch more than expected).

Fields of papers citing papers by Tom Brosch

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tom Brosch. 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 Tom Brosch. The network helps show where Tom Brosch may publish in the future.

Co-authorship network of co-authors of Tom Brosch

This figure shows the co-authorship network connecting the top 25 collaborators of Tom Brosch. A scholar is included among the top collaborators of Tom Brosch 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 Tom Brosch. Tom Brosch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Arasteh, Soroosh Tayebi, Danielle F. Pace, Polina Golland, et al.. (2023). Automated segmentation of 3D cine cardiovascular magnetic resonance imaging. Frontiers in Cardiovascular Medicine. 10. 1167500–1167500. 6 indexed citations

Pace, Danielle F., Adrian V. Dalca, Tom Brosch, et al.. (2022). Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease. Medical Image Analysis. 80. 102469–102469. 10 indexed citations

Iuga, Andra-Iza, Tom Brosch, Tobias Klinder, et al.. (2021). Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks. BMC Medical Imaging. 21(1). 69–69. 15 indexed citations

Brosch, Tom, Jörg Peters, Alexandra Groth, F. Weber, & Jürgen Weese. (2021). Model-based segmentation using neural network-based boundary detectors: Application to prostate and heart segmentation in MR images. SHILAP Revista de lepidopterología. 6. 100078–100078. 5 indexed citations

Sommer, Karsten, Axel Saalbach, Tom Brosch, et al.. (2020). Correction of Motion Artifacts Using a Multiscale Fully Convolutional Neural Network. American Journal of Neuroradiology. 41(3). 416–423. 32 indexed citations

Iuga, Andra-Iza, Tom Brosch, Rafael Wiemker, et al.. (2020). Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks. 44–44. 4 indexed citations

Tang, Lisa, Enedino Hernández‐Torres, Tom Brosch, et al.. (2019). FLAIR2 improves LesionTOADS automatic segmentation of multiple sclerosis lesions in non-homogenized, multi-center, 2D clinical magnetic resonance images. NeuroImage Clinical. 23. 101918–101918. 9 indexed citations

Brosch, Tom, et al.. (2018). Organ-At-Risk Segmentation in Brain MRI Using Model-Based Segmentation: Benefits of Deep Learning-Based Boundary Detectors. Lecture notes in computer science. 11167. 291–299. 4 indexed citations

Pace, Danielle F., Adrian V. Dalca, Tom Brosch, et al.. (2018). Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease. Lecture notes in computer science. 11045. 334–342. 14 indexed citations

10.

Heinrich‬, Mattias P., et al.. (2018). Nearest neighbor 3D segmentation with context features. 21–21. 1 indexed citations

11.

Brosch, Tom & Axel Saalbach. (2018). Foveal fully convolutional nets for multi-organ segmentation. 29–29. 15 indexed citations

12.

Lorenz, Cristian, Tom Brosch, Thierry Léfèvre, et al.. (2018). Automated abdominal plane and circumference estimation in 3D US for fetal screening. 17–17. 9 indexed citations

13.

Groza, Vladimir, Tom Brosch, Dennis Eschweiler, et al.. (2018). Comparison of deep learning-based techniques for organ segmentation in abdominal CT images. 6 indexed citations

14.

Yoo, Youngjin, Lisa Tang, Tom Brosch, et al.. (2017). Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. NeuroImage Clinical. 17. 169–178. 62 indexed citations

15.

Brosch, Tom. (2016). Efficient deep learning of 3D structural brain MRIs for manifold learning and lesion segmentation with application to multiple sclerosis. Open Collections. 1 indexed citations

16.

Brosch, Tom, Lisa Tang, Youngjin Yoo, et al.. (2016). Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation. IEEE Transactions on Medical Imaging. 35(5). 1229–1239. 306 indexed citations breakdown →

17.

Brosch, Tom & Roger Tam. (2014). Efficient Training of Convolutional Deep Belief Networks in the Frequency Domain for Application to High-Resolution 2D and 3D Images. Neural Computation. 27(1). 211–227. 40 indexed citations

18.

Brosch, Tom, Youngjin Yoo, David K.B. Li, Anthony Traboulsee, & Roger Tam. (2014). Modeling the Variability in Brain Morphology and Lesion Distribution in Multiple Sclerosis by Deep Learning. Lecture notes in computer science. 17(Pt 2). 462–469. 32 indexed citations

19.

Brosch, Tom, et al.. (2013). Manifold Learning of Brain MRIs by Deep Learning. Lecture notes in computer science. 16(Pt 2). 633–640. 128 indexed citations

20.

Brosch, Tom, et al.. (2007). MALWARE REMOVAL - BEYOND CONTENT AND CONTEXT SCANNING.

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.

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