Suprosanna Shit

3.0k total citations
29 papers, 469 citations indexed

About

Suprosanna Shit is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Suprosanna Shit has authored 29 papers receiving a total of 469 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 13 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Artificial Intelligence. Recurrent topics in Suprosanna Shit's work include Medical Image Segmentation Techniques (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Medical Imaging and Analysis (4 papers). Suprosanna Shit is often cited by papers focused on Medical Image Segmentation Techniques (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Medical Imaging and Analysis (4 papers). Suprosanna Shit collaborates with scholars based in Germany, Switzerland and United Kingdom. Suprosanna Shit's co-authors include Bjoern Menze, Johannes C. Paetzold, Marie Piraud, Ali Ertürk, Oliver Schoppe, Giles Tetteh, Marco Düring, Martin Dichgans, Katalin Völgyi and Mihail Ivilinov Todorov and has published in prestigious journals such as Nature Methods, IEEE Transactions on Medical Imaging and Human Brain Mapping.

In The Last Decade

Suprosanna Shit

25 papers receiving 462 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Suprosanna Shit Germany 10 171 126 97 91 65 29 469
Saad Nadeem United States 12 165 1.0× 90 0.7× 36 0.4× 44 0.5× 96 1.5× 45 490
Fenqiang Zhao United States 10 166 1.0× 110 0.9× 90 0.9× 128 1.4× 61 0.9× 32 433
Julien Dauguet France 11 288 1.7× 119 0.9× 58 0.6× 65 0.7× 33 0.5× 25 756
Sushmita Datta United States 17 342 2.0× 193 1.5× 98 1.0× 26 0.3× 49 0.8× 27 933
Bistra Iordanova United States 11 278 1.6× 120 1.0× 67 0.7× 36 0.4× 50 0.8× 24 792
Sharmishtaa Seshamani United States 10 136 0.8× 115 0.9× 81 0.8× 241 2.6× 50 0.8× 24 733
Xabier Artaechevarria Spain 7 189 1.1× 297 2.4× 81 0.8× 25 0.3× 55 0.8× 10 563
Eloy Roura Spain 11 208 1.2× 297 2.4× 73 0.8× 36 0.4× 101 1.6× 18 666
Blake E. Dewey United States 20 694 4.1× 303 2.4× 140 1.4× 74 0.8× 128 2.0× 59 1.3k
Daniel Goldberg‐Zimring United States 11 212 1.2× 96 0.8× 82 0.8× 24 0.3× 40 0.6× 16 481

Countries citing papers authored by Suprosanna Shit

Since Specialization
Citations

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

Fields of papers citing papers by Suprosanna Shit

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Suprosanna Shit

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

All Works

20 of 20 papers shown
1.
Hager, Paul, et al.. (2025). Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging. Medical Image Analysis. 101. 103451–103451. 1 indexed citations
2.
Amiranashvili, Tamaz, et al.. (2025). vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation. 20874–20884.
3.
He, Hailong, Johannes C. Paetzold, Ivan Ezhov, et al.. (2024). Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for Automated Extraction of Psoriasis and Aging Biomarkers. IEEE Transactions on Medical Imaging. 43(6). 2074–2085. 7 indexed citations
4.
Ezhov, Ivan, Florian Kofler, Suprosanna Shit, et al.. (2024). Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue. Journal of Biomedical Optics. 29(9). 93509–93509. 2 indexed citations
5.
Navarro, Fernando, et al.. (2023). Focused Decoding Enables 3D Anatomical Detection by Transformers. Zurich Open Repository and Archive (University of Zurich). 2(February 2023). 72–95. 7 indexed citations
6.
Kofler, Florian, Ivan Ezhov, Fabian Isensee, et al.. (2023). Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient. Zurich Open Repository and Archive (University of Zurich). 2(May 2023). 27–71. 14 indexed citations
7.
Schlaeger, Sarah, Suprosanna Shit, Paul Eichinger, et al.. (2023). AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis. Insights into Imaging. 14(1). 123–123. 7 indexed citations
8.
9.
Ezhov, Ivan, Suprosanna Shit, Frédéric Lange, et al.. (2023). Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis. PuSH - Publication Server of Helmholtz Zentrum München. 78–78. 4 indexed citations
10.
Menten, Martin J., Johannes C. Paetzold, Veronika A. Zimmer, et al.. (2023). A skeletonization algorithm for gradient-based optimization. 21337–21346. 6 indexed citations
11.
Ezhov, Ivan, Suprosanna Shit, Jana Lipková, et al.. (2022). Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling. Medical Image Analysis. 83. 102672–102672. 14 indexed citations
12.
Navarro, Fernando, Suprosanna Shit, Anjany Sekuboyina, et al.. (2022). A Unified 3D Framework for Organs-at-Risk Localization and Segmentation for Radiation Therapy Planning. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 1544–1547. 3 indexed citations
13.
Shit, Suprosanna, et al.. (2022). SRflow: Deep learning based super-resolution of 4D-flow MRI data. Frontiers in Artificial Intelligence. 5. 928181–928181. 14 indexed citations
14.
Navarro, Fernando, Suprosanna Shit, Anjany Sekuboyina, et al.. (2022). Self-Supervised Pretext Tasks in Model Robustness & Generalizability: A Revisit from Medical Imaging Perspective. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 5074–5079. 3 indexed citations
15.
Li, Hongwei, Aurore Menegaux, Benita Schmitz‐Koep, et al.. (2021). Automated claustrum segmentation in human brain MRI using deep learning. Human Brain Mapping. 42(18). 5862–5872. 18 indexed citations
16.
Ezhov, Ivan, Suprosanna Shit, Jana Lipková, et al.. (2021). Geometry-Aware Neural Solver for Fast Bayesian Calibration of Brain Tumor Models. IEEE Transactions on Medical Imaging. 41(5). 1269–1278. 13 indexed citations
17.
Kofler, Florian, Ivan Ezhov, Lucas Fidon, et al.. (2021). Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles. Frontiers in Neuroscience. 15. 752780–752780. 2 indexed citations
18.
Todorov, Mihail Ivilinov, Johannes C. Paetzold, Oliver Schoppe, et al.. (2020). Machine learning analysis of whole mouse brain vasculature. Nature Methods. 17(4). 442–449. 205 indexed citations
19.
Mukherjee, Subhadip, Suprosanna Shit, & Chandra Sekhar Seelamantula. (2018). Phasesplit: A Variable Splitting Framework for Phase Retrieval. 4709–4713. 1 indexed citations
20.

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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