Phillip Chlap

1.3k citations
30 papers · 792 · 1 hit paper · h-index 10

Impact in

Papers in

Phillip Chlap

29 papers receiving 779 citations

Hit Papers

A review of medical image data augmentation techniques for deep learning applications 2021 · 583 citations
5830+1+3Years since publication100200300400500

Peers

Phillip Chlap
Comparison fields: 5 of 132
  • Health Informatics 40
  • Radiology, Nuclear Medicine and Imaging 359
  • Radiation 96
  • Neurology 79
  • Computer Vision and Pattern Recognition 182
Replace Wenjian Qin with:
Wenjian Qin China
Mohammad Hesam Hesamian Australia
Ester Bonmati United Kingdom
Daniel Forsberg Sweden
Matthias Wilms Canada
Kelei He China
João Otávio Bandeira Diniz Brazil
Xiaowei Ding China
Seyed‐Ahmad Ahmadi Germany
Phillip Chlap relative to Wenjian Qin China Wenjian Qin's profile →
Citations per field
00.5×1.7×
Wenjian Qin · 1×
Citations per year

Countries citing papers authored by Phillip Chlap

Since Specialization
Citations

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

Fields of papers citing papers by Phillip Chlap

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A review of medical image data augmentation techniques for deep learning applications
Hit paper breakdown →
2021583
2 202126
3 202225
4 201823
5 202319
6 202316
7 202312
8 202111
9 20239
10 20239
11 20238
12 20237
13 20227
14 20215
15 20225
16 20234
17 20204
18 20244
19 20223
20 20232

About Phillip Chlap

Phillip Chlap is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Radiation, Artificial Intelligence and Biomedical Engineering, having authored 30 papers that have together received 792 indexed citations. Recurring topics across this work include Advanced Radiotherapy Techniques (16 papers), Radiomics and Machine Learning in Medical Imaging (16 papers), Lung Cancer Diagnosis and Treatment (10 papers), Medical Imaging Techniques and Applications (7 papers), Radiation Therapy and Dosimetry (4 papers), AI in cancer detection (4 papers), Advanced X-ray and CT Imaging (3 papers) and Prostate Cancer Diagnosis and Treatment (3 papers). The work is most often cited by research in Health Informatics (40 citations), Radiology, Nuclear Medicine and Imaging (359 citations), Radiation (96 citations), Neurology (79 citations) and Computer Vision and Pattern Recognition (182 citations). Phillip Chlap has collaborated with scholars based in Australia, United Kingdom and United States. Frequent co-authors include Lois Holloway, Jason Dowling, Hang Min, Annette Haworth, Shalini Vinod, Michael Jameson, Geoff P. Delaney, Ali Haidar, Arcot Sowmya and David Thwaites. Their work appears in journals such as Radiotherapy and Oncology, Clinical Oncology, Medical Physics, Journal of Applied Clinical Medical Physics and Cell Reports.

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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