Sheeba J. Sujit

675 total citations
10 papers, 304 citations indexed

About

Sheeba J. Sujit is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pathology and Forensic Medicine. According to data from OpenAlex, Sheeba J. Sujit has authored 10 papers receiving a total of 304 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 5 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Pathology and Forensic Medicine. Recurrent topics in Sheeba J. Sujit's work include AI in cancer detection (5 papers), Ultrasound Imaging and Elastography (4 papers) and Multiple Sclerosis Research Studies (3 papers). Sheeba J. Sujit is often cited by papers focused on AI in cancer detection (5 papers), Ultrasound Imaging and Elastography (4 papers) and Multiple Sclerosis Research Studies (3 papers). Sheeba J. Sujit collaborates with scholars based in United States, Germany and United Kingdom. Sheeba J. Sujit's co-authors include Ivan Coronado, Ponnada A. Narayana, Refaat E. Gabr, Jerry S. Wolinsky, Fred Lublin, Xiaojun Sun, Arash Kamali, Sushmita Datta, Melvin Robinson and Hai T. Tran and has published in prestigious journals such as Radiology, Journal of Allergy and Clinical Immunology and Annals of Oncology.

In The Last Decade

Sheeba J. Sujit

9 papers receiving 299 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sheeba J. Sujit United States	7	117	83	83	71	50	10	304
Béatrice Carsin France	8	142 1.2×	43 0.5×	41 0.5×	70 1.0×	37 0.7×	9	305
David Laidley Canada	11	104 0.9×	51 0.6×	50 0.6×	40 0.6×	47 0.9×	32	456
Esther Alberts Germany	6	161 1.4×	34 0.4×	71 0.9×	67 0.9×	36 0.7×	8	297
A. Saura Quiles Spain	5	58 0.5×	73 0.9×	68 0.8×	105 1.5×	23 0.5×	10	243
Paul Eichinger Germany	9	201 1.7×	109 1.3×	30 0.4×	32 0.5×	22 0.4×	12	314
Jacob C. Reinhold United States	4	210 1.8×	28 0.3×	46 0.6×	105 1.5×	86 1.7×	7	393
Longzheng Tong China	7	121 1.0×	40 0.5×	86 1.0×	57 0.8×	26 0.5×	12	392
Taiki Magome Japan	11	226 1.9×	30 0.4×	66 0.8×	65 0.9×	43 0.9×	37	394
Simon Francis Canada	4	149 1.3×	102 1.2×	128 1.5×	191 2.7×	69 1.4×	8	398
Ezequiel Geremia France	4	80 0.7×	34 0.4×	126 1.5×	197 2.8×	69 1.4×	6	303

Countries citing papers authored by Sheeba J. Sujit

Since Specialization

Citations

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

Fields of papers citing papers by Sheeba J. Sujit

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sheeba J. Sujit. 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 Sheeba J. Sujit. The network helps show where Sheeba J. Sujit may publish in the future.

Co-authorship network of co-authors of Sheeba J. Sujit

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

All Works

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10 of 10 papers shown

Moss, Ronald B., Sheeba J. Sujit, Paul Tonks, et al.. (2025). Development of Pan-H5 Vaccines Against Avian Influenza Developed Using Computational Biology to Mitigate Future Pandemics. Journal of Allergy and Clinical Immunology. 155(2). AB448–AB448.

Tran, Hai T., Simon Heeke, Sheeba J. Sujit, et al.. (2023). Circulating tumor DNA and radiological tumor volume identify patients at risk for relapse with resected, early-stage non-small-cell lung cancer. Annals of Oncology. 35(2). 183–189. 32 indexed citations

Sujit, Sheeba J., et al.. (2021). Deep learning enabled brain shunt valve identification using mobile phones. Computer Methods and Programs in Biomedicine. 210. 106356–106356. 5 indexed citations

Narayana, Ponnada A., Ivan Coronado, Sheeba J. Sujit, et al.. (2019). Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI. Radiology. 294(2). 398–404. 80 indexed citations

Narayana, Ponnada A., Ivan Coronado, Sheeba J. Sujit, et al.. (2019). Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning. Magnetic Resonance Imaging. 65. 8–14. 24 indexed citations

Sujit, Sheeba J., Ivan Coronado, Arash Kamali, Ponnada A. Narayana, & Refaat E. Gabr. (2019). Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks. Journal of Magnetic Resonance Imaging. 50(4). 1260–1267. 49 indexed citations

Narayana, Ponnada A., Ivan Coronado, Sheeba J. Sujit, et al.. (2019). Deep‐Learning‐Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size. Journal of Magnetic Resonance Imaging. 51(5). 1487–1496. 39 indexed citations

Gabr, Refaat E., Ivan Coronado, Melvin Robinson, et al.. (2019). Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study. Multiple Sclerosis Journal. 26(10). 1217–1226. 61 indexed citations

Sujit, Sheeba J., Refaat E. Gabr, Ivan Coronado, et al.. (2018). Automated Image Quality Evaluation of Structural Brain Magnetic Resonance Images using Deep Convolutional Neural Networks. 33–36. 8 indexed citations

10.

Narayana, Ponnada A., Ivan Coronado, Melvin Robinson, et al.. (2018). Multimodal MRI Segmentation of Brain Tissue and T2-Hyperintense White Matter Lesions in Multiple Sclerosis using Deep Convolutional Neural Networks and a Large Multi-center Image Database. 13–16. 6 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.

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