Maxim Berman
Impact in
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- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Advanced Image and Video Retrieval Techniques
- Media Technology top 5%
Papers in
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- Advanced Neural Network Applications 4
- Medical Image Segmentation Techniques 1
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- Machine Learning and Algorithms 3
- Stochastic Gradient Optimization Techniques 2
- Machine Learning and Data Classification 1
- Neural Networks and Applications 1
- Co-authors
- Matthew B. Blaschko (6 shared papers)Amal Rannen Triki (3 shared papers)Jeroen Bertels (1 shared paper)Tom Eelbode (1 shared paper)Dirk Vandermeulen (1 shared paper)Raf Bisschops (1 shared paper)Frederik Maes (1 shared paper)Christos Sagonas (1 shared paper)
- Journals
- IEEE Transactions on Medical Imaging (1 paper)Lirias (KU Leuven) (3 papers)arXiv (Cornell University) (2 papers)
In The Last Decade
Maxim Berman
6 papers receiving 819 citations
Maxim Berman's Hit Papers
Peers
Comparison fields: 5 of 115
- Computer Vision and Pattern Recognition 425
- Media Technology 88
- Health Informatics 11
- Geology 45
- Environmental Engineering 112
Countries citing papers authored by Maxim Berman
This map shows the geographic impact of Maxim Berman'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 Maxim Berman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maxim Berman more than expected).
Fields of papers citing papers by Maxim Berman
This network shows the impact of papers produced by Maxim Berman. 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 Maxim Berman. The network helps show where Maxim Berman may publish in the future.
Co-authors
The 9 scholars most cited alongside Maxim Berman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks Hit paper breakdown → | 2018 | 570 |
| 2 | Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index Hit paper breakdown → | 2020 | 250 |
| 3 | 2019 | 16 | |
| 4 | Optimization of the Jaccard index for image segmentation with the Lovász hinge. | 2017 | 9 |
| 5 | Stochastic Weighted Function Norm Regularization. | 2017 | 1 |
| 6 | 2019 | 1 |
About Maxim Berman
Maxim Berman is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering, having authored 6 papers that have together received 847 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Machine Learning and Algorithms (3 papers), Stochastic Gradient Optimization Techniques (2 papers), COVID-19 diagnosis using AI (1 paper), Machine Learning and Data Classification (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper), Neural Networks and Applications (1 paper) and Medical Image Segmentation Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (425 citations), Media Technology (88 citations), Health Informatics (11 citations), Geology (45 citations) and Environmental Engineering (112 citations). Maxim Berman has collaborated with scholars based in Belgium and Austria. Frequent co-authors include Matthew B. Blaschko, Amal Rannen Triki, Jeroen Bertels, Tom Eelbode, Dirk Vandermeulen, Raf Bisschops, Frederik Maes, Christos Sagonas and Vladimir Kolmogorov. Their work appears in journals such as IEEE Transactions on Medical Imaging, Lirias (KU Leuven) and arXiv (Cornell University).
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.