Troy Long

13 papers receiving 845 citations

Troy Long's Hit Papers

A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning 2019 · 261 citations
2610+2+4Years since publication50100150200250

Peers

Troy Long
Comparison fields: 5 of 64
  • Radiation 640
  • Health Informatics 29
  • Radiology, Nuclear Medicine and Imaging 294
  • Pulmonary and Respiratory Medicine 251
  • Otorhinolaryngology 14
Replace Jianrong Dai with:
Jianrong Dai China
Linghong Zhou China
Robert Jacques United States
Mark Bangert Germany
Jason Xie Canada
Kevin Souris Belgium
R Siochi United States
Sebastiaan Breedveld Netherlands
Saikit Lam Hong Kong
Patricio Simari United States
Troy Long relative to Jianrong Dai China Jianrong Dai's profile →
Citations per field
00.5×11.4×
Jianrong Dai · 1×
Citations per year

Countries citing papers authored by Troy Long

Since Specialization
Citations

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

Fields of papers citing papers by Troy Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Troy Long, 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 Troy Long Line = papers co-authored together Troy Long links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1
A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning
Hit paper breakdown →
2019261
2 2012140
3 2013117
4 201488
5 201464
6 201847
7 201340
8
Dose Prediction with U-net: A Feasibility Study for Predicting Dose Distributions from Contours using Deep Learning on Prostate IMRT Patients.
201724
9 201322
10 201321
11 201812
12 201811
13 20166

About Troy Long

Troy Long is a scholar working on Radiation, Biomedical Engineering, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 13 papers that have together received 853 indexed citations. Recurring topics across this work include Advanced Radiotherapy Techniques (12 papers), Advanced X-ray and CT Imaging (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Machine Learning and Algorithms (1 paper), Wireless Body Area Networks (1 paper), Context-Aware Activity Recognition Systems (1 paper), Statistical Methods in Clinical Trials (1 paper) and Medical Imaging Techniques and Applications (1 paper). The work is most often cited by research in Radiation (640 citations), Health Informatics (29 citations), Radiology, Nuclear Medicine and Imaging (294 citations), Pulmonary and Respiratory Medicine (251 citations) and Otorhinolaryngology (14 citations). Troy Long has collaborated with scholars based in United States, Germany and China. Frequent co-authors include Steve Jiang, Xun Jia, Dan Nguyen, Weiguo Lu, Zohaib Iqbal, Xuejun Gu, Ke Sheng, Dan Ruan, Edwin Romeijn and Daniel A. Low. Their work appears in journals such as Medical Physics, International Journal of Radiation Oncology*Biology*Physics, GigaScience, PLoS ONE and Physics in Medicine and Biology.

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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