Daniel Marcus

992 total citations
9 papers, 174 citations indexed

About

Daniel Marcus is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Cognitive Neuroscience. According to data from OpenAlex, Daniel Marcus has authored 9 papers receiving a total of 174 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Radiology, Nuclear Medicine and Imaging, 3 papers in Genetics and 2 papers in Cognitive Neuroscience. Recurrent topics in Daniel Marcus's work include Radiomics and Machine Learning in Medical Imaging (3 papers), Glioma Diagnosis and Treatment (3 papers) and Advanced MRI Techniques and Applications (3 papers). Daniel Marcus is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (3 papers), Glioma Diagnosis and Treatment (3 papers) and Advanced MRI Techniques and Applications (3 papers). Daniel Marcus collaborates with scholars based in United States, United Kingdom and Netherlands. Daniel Marcus's co-authors include David C. Van Essen, Jennifer Stine Elam, Kâmil Uǧurbil, Michael P. Harms, Gregory C. Burgess, Matthew F. Glasser, Essa Yacoub, Michael R. Hodge, Jan‐Mathijs Schoffelen and Stephen M. Smith and has published in prestigious journals such as NeuroImage, Cancer and Human Brain Mapping.

In The Last Decade

Daniel Marcus

8 papers receiving 170 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Marcus United States	3	98	71	19	18	17	9	174
Marie Song United Kingdom	4	143 1.5×	228 3.2×	24 1.3×	24 1.3×	34 2.0×	6	311
Giovanni Savini Italy	9	60 0.6×	77 1.1×	12 0.6×	8 0.4×	5 0.3×	18	183
P. Hawse United States	4	89 0.9×	153 2.2×	14 0.7×	7 0.4×	14 0.8×	7	433
Miklos Espak United Kingdom	4	38 0.4×	51 0.7×	13 0.7×	58 3.2×	11 0.6×	4	182
Bettina L. Serrallach Switzerland	8	101 1.0×	28 0.4×	9 0.5×	30 1.7×	6 0.4×	25	200
Fan Mo China	8	131 1.3×	54 0.8×	8 0.4×	27 1.5×	4 0.2×	14	233
Rosa Pasquariello Italy	11	53 0.5×	47 0.7×	10 0.5×	27 1.5×	39 2.3×	43	257
Luís Miguel Lacerda United Kingdom	7	130 1.3×	129 1.8×	10 0.5×	23 1.3×	3 0.2×	13	229
Jinhong Wang China	11	133 1.4×	60 0.8×	16 0.8×	46 2.6×	4 0.2×	15	297
Sunil K. Vasireddi United States	7	68 0.7×	100 1.4×	18 0.9×	15 0.8×	5 0.3×	15	286

Countries citing papers authored by Daniel Marcus

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Marcus

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Marcus

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Marcus. A scholar is included among the top collaborators of Daniel Marcus 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 Daniel Marcus. Daniel Marcus 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:

9 of 9 papers shown

1.

Xiao, Pan, Jieqi Wang, Vamsi R. Narra, et al.. (2023). A generative-discriminative deep learning approach to classify radiology reports based on the presence of follow up recommendations. 24–24.

2.

Elam, Jennifer Stine, Matthew F. Glasser, Michael P. Harms, et al.. (2021). The Human Connectome Project: A retrospective. NeuroImage. 244. 118543–118543. 127 indexed citations

3.

Chakrabarty, Satrajit, Aristeidis Sotiras, Mikhail Milchenko, et al.. (2021). BrainTumorNet: multi-task learning for joint segmentation of high-grade glioma and brain metastases from MR images. 13–13. 1 indexed citations

4.

Milchenko, Mikhail, Pamela LaMontagne, & Daniel Marcus. (2020). Automatic detection of contrast enhancement in T1-weighted brain MRI of human adults. 14–14. 1 indexed citations

5.

Thakur, Siddhesh, Jimit Doshi, Sung Min Ha, et al.. (2019). NIMG-40. ROBUST MODALITY-AGNOSTIC SKULL-STRIPPING IN PRESENCE OF DIFFUSE GLIOMA: A MULTI-INSTITUTIONAL STUDY. Neuro-Oncology. 21(Supplement_6). vi170–vi170. 2 indexed citations

6.

Kochunov, Peter, Braxton D. Mitchell, Habib Ganjgahi, et al.. (2018). Genomic kinship construction to enhance genetic analyses in the human connectome project data. Human Brain Mapping. 40(5). 1677–1688. 13 indexed citations

7.

Marcus, Daniel. (1992). Play therapy with young children. The Indian Journal of Pediatrics. 59(1). 53–60. 2 indexed citations

8.

Arens, Raanan, Daniel Marcus, Santiago Engelberg, et al.. (1988). Cerebral germinomas and klinefelter syndrome.A review. Cancer. 61(6). 1228–1231. 26 indexed citations

9.

Kasper, Wolfgang, et al.. (1981). Posterior aortic wall motion and left atrial volume changes.. PubMed. 70(4). 245–9. 2 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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