James G. Malcolm

2.5k citations
68 papers · 1.8k · h-index 21

Impact in

Papers in

James G. Malcolm

62 papers receiving 1.8k citations

Peers

James G. Malcolm
Comparison fields: 5 of 117
  • Computational Mathematics 53
  • Neurology 520
  • Radiology, Nuclear Medicine and Imaging 634
  • Computer Vision and Pattern Recognition 250
  • Genetics 96
Replace Peter Hastreiter with:
Peter Hastreiter Germany
A. Nabavi Germany
Jan Klein Germany
F.A. Jolesz United States
Hatsuho Mamata United States
Yan Jin United States
Lisa Jonasson Switzerland
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Benoît Scherrer United States
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James G. Malcolm relative to Peter Hastreiter Germany Peter Hastreiter's profile →
Citations per field
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Citations per year

Countries citing papers authored by James G. Malcolm

Since Specialization
Citations

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

Fields of papers citing papers by James G. Malcolm

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James G. Malcolm. 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 James G. Malcolm. The network helps show where James G. Malcolm may publish in the future.

Co-authors

The 25 scholars most cited alongside James G. Malcolm, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with James G. Malcolm Line = papers co-authored together James G. Malcolm links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 68 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011321
2 2010194
3 2016138
4 2017124
5 201781
6 201880
7 201671
8 200771
9 200754
10 201948
11 201746
12 202239
13 201637
14 201035
15 200935
16 200734
17 200834
18 200932
19 201732
20 201023

About James G. Malcolm

James G. Malcolm is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Neurology, Pathology and Forensic Medicine and Computational Mathematics, having authored 68 papers that have together received 1.8k indexed citations. Recurring topics across this work include Advanced Neuroimaging Techniques and Applications (15 papers), Medical Image Segmentation Techniques (11 papers), Advanced MRI Techniques and Applications (9 papers), Spine and Intervertebral Disc Pathology (8 papers), Tensor decomposition and applications (8 papers), Traumatic Brain Injury and Neurovascular Disturbances (7 papers), Image Retrieval and Classification Techniques (6 papers) and Advanced Vision and Imaging (5 papers). The work is most often cited by research in Computational Mathematics (53 citations), Neurology (520 citations), Radiology, Nuclear Medicine and Imaging (634 citations), Computer Vision and Pattern Recognition (250 citations) and Genetics (96 citations). James G. Malcolm has collaborated with scholars based in United States, Canada and Israel. Frequent co-authors include Yogesh Rathi, Martha E. Shenton, Allen Tannenbaum, Faiz U. Ahmad, Gustavo Pradilla, Rima S. Rindler, Jonathan A Grossberg, Jason Chu, Alonso Ramírez-Manzanares and Fatima Tensaouti. Their work appears in journals such as Neurosurgery, World Neurosurgery, Journal of Neuro-Oncology, IEEE Transactions on Control Systems Technology and JAMA Surgery.

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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