Phillip Chlap

1.3k total citations · 1 hit paper
30 papers, 792 citations indexed

About

Phillip Chlap is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Radiation. According to data from OpenAlex, Phillip Chlap has authored 30 papers receiving a total of 792 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Radiology, Nuclear Medicine and Imaging, 16 papers in Pulmonary and Respiratory Medicine and 16 papers in Radiation. Recurrent topics in Phillip Chlap's work include Radiomics and Machine Learning in Medical Imaging (16 papers), Advanced Radiotherapy Techniques (16 papers) and Lung Cancer Diagnosis and Treatment (10 papers). Phillip Chlap is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (16 papers), Advanced Radiotherapy Techniques (16 papers) and Lung Cancer Diagnosis and Treatment (10 papers). Phillip Chlap collaborates with scholars based in Australia, United Kingdom and United States. Phillip Chlap's co-authors include Lois Holloway, Jason Dowling, Annette Haworth, Hang Min, Shalini Vinod, Michael Jameson, Geoff P. Delaney, Ali Haidar, Eng‐Siew Koh and Paul Keall and has published in prestigious journals such as Cell Reports, Physics in Medicine and Biology and Medical Physics.

In The Last Decade

Phillip Chlap

29 papers receiving 779 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A review of medical image data augmentation techniques for deep learning applications

2021 583 citations Phillip Chlap, Hang Min et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Phillip Chlap Australia	10	359	198	182	117	112	30	792
Shekhar S. Chandra Australia	15	347 1.0×	146 0.7×	192 1.1×	244 2.1×	91 0.8×	70	876
Davood Karimi Canada	14	392 1.1×	309 1.6×	176 1.0×	155 1.3×	138 1.2×	46	795
Mohammad Hesam Hesamian Australia	5	517 1.4×	378 1.9×	445 2.4×	224 1.9×	140 1.3×	11	1.3k
Kelei He China	16	580 1.6×	369 1.9×	441 2.4×	210 1.8×	135 1.2×	25	1.1k
Xiaowei Ding China	5	441 1.2×	364 1.8×	364 2.0×	114 1.0×	67 0.6×	8	825
Wenjun Tan China	18	366 1.0×	149 0.8×	207 1.1×	173 1.5×	172 1.5×	94	996
Darvin Yi United States	12	778 2.2×	359 1.8×	246 1.4×	149 1.3×	184 1.6×	26	1.2k
Daniel Forsberg Sweden	14	370 1.0×	119 0.6×	177 1.0×	329 2.8×	73 0.7×	37	988
Seyed‐Ahmad Ahmadi Germany	18	304 0.8×	180 0.9×	339 1.9×	190 1.6×	67 0.6×	48	1.2k

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-authorship network of co-authors of Phillip Chlap

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

All Works

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20 of 20 papers shown

Chlap, Phillip, et al.. (2024). PyDicer: An open-source python library for conversion and analysis of radiotherapy DICOM data. SoftwareX. 29. 102010–102010. 1 indexed citations

Chlap, Phillip, Lois Holloway, David Thwaites, et al.. (2024). Dosimetric Impact of Delineation and Motion Uncertainties on the Heart and Substructures in Lung Cancer Radiotherapy. Clinical Oncology. 36(7). 420–429. 1 indexed citations

Chlap, Phillip, Eric Hau, Xinliang Ma, et al.. (2024). Cardiac Substructure Dose and Survival in Stereotactic Radiotherapy for Lung Cancer: Results of the Multi-Centre SSBROC Trial. Clinical Oncology. 36(10). 642–650. 1 indexed citations

Chlap, Phillip, et al.. (2024). 636: Dosimetric factors impacting urethral toxicity following stereotactic prostate radiotherapy. Radiotherapy and Oncology. 194. S2343–S2345. 1 indexed citations

Chlap, Phillip, Hang Min, Jason Dowling, et al.. (2024). Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets. Computerized Medical Imaging and Graphics. 116. 102403–102403. 4 indexed citations

Haidar, Ali, Matthew Field, Vikneswary Batumalai, et al.. (2023). Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach. Cancers. 15(3). 564–564. 4 indexed citations

Min, Hang, Jason Dowling, Michael Jameson, et al.. (2023). Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation. Radiotherapy and Oncology. 186. 109794–109794. 9 indexed citations

Chlap, Phillip, Ali Haidar, James Otton, et al.. (2023). Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation. Physical and Engineering Sciences in Medicine. 46(1). 377–393. 19 indexed citations

Arumugam, Sankar, et al.. (2023). Assessment of intrafraction motion and its dosimetric impact on prostate radiotherapy using an in-house developed position monitoring system. Frontiers in Oncology. 13. 1082391–1082391. 2 indexed citations

10.

Lee, Mark, Michael Jameson, Phillip Chlap, et al.. (2023). Mid-treatment 18F-FDG PET imaging changes in parotid gland correlates to radiation-induced xerostomia. Radiotherapy and Oncology. 186. 109745–109745. 2 indexed citations

11.

Deshpande, Shriprasad R., et al.. (2023). PO-1633 Clinical evaluation of deep learning-based nodal structures segmentation for gynecological cancers. Radiotherapy and Oncology. 182. S1329–S1330. 1 indexed citations

12.

Chen, Cheng, et al.. (2023). Realistic CT data augmentation for accurate deep‐learning based segmentation of head and neck tumors in kV images acquired during radiation therapy. Medical Physics. 50(7). 4206–4219. 8 indexed citations

13.

Keall, Paul, Michael Jameson, Daniel Moses, et al.. (2023). Changes in serial multiparametric MRI and FDG-PET/CT functional imaging during radiation therapy can predict treatment response in patients with head and neck cancer. European Radiology. 33(12). 8788–8799. 9 indexed citations

14.

Chlap, Phillip, et al.. (2023). PlatiPy: Processing Library and Analysis Toolkit forMedical Imaging in Python. The Journal of Open Source Software. 8(86). 5374–5374. 16 indexed citations

15.

Holloway, Lois, et al.. (2022). Optimal and actual rates of Stereotactic Ablative Body Radiotherapy (SABR) utilisation for primary lung cancer in Australia. Clinical and Translational Radiation Oncology. 34. 7–14. 3 indexed citations

16.

Samarasinghe, Gihan, Michael Jameson, Lois Holloway, et al.. (2021). Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images. Magnetic Resonance Imaging. 86. 28–36. 26 indexed citations

17.

Min, Hang, Jason Dowling, Michael Jameson, et al.. (2021). Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial. Physics in Medicine and Biology. 66(19). 195008–195008. 11 indexed citations

18.

Kumar, Shivani, Lois Holloway, Miriam M. Boxer, et al.. (2021). Variability of gross tumour volume delineation: MRI and CT based tumour and lymph node delineation for lung radiotherapy. Radiotherapy and Oncology. 167. 292–299. 5 indexed citations

19.

Dowling, Jason, et al.. (2020). Assessing tumor centrality in lung stereotactic ablative body radiotherapy (SABR): the effects of variations in bronchial tree delineation and potential for automated methods. Medical dosimetry. 46(1). 94–101. 4 indexed citations

20.

Middleton, A., Cristina Dal Bosco, Phillip Chlap, et al.. (2018). Data-Driven Modeling of Intracellular Auxin Fluxes Indicates a Dominant Role of the ER in Controlling Nuclear Auxin Uptake. Cell Reports. 22(11). 3044–3057. 23 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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