Mary Rickard

1.4k total citations
60 papers, 882 citations indexed

About

Mary Rickard is a scholar working on Pulmonary and Respiratory Medicine, Oncology and Artificial Intelligence. According to data from OpenAlex, Mary Rickard has authored 60 papers receiving a total of 882 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Pulmonary and Respiratory Medicine, 28 papers in Oncology and 23 papers in Artificial Intelligence. Recurrent topics in Mary Rickard's work include Global Cancer Incidence and Screening (28 papers), Digital Radiography and Breast Imaging (27 papers) and AI in cancer detection (23 papers). Mary Rickard is often cited by papers focused on Global Cancer Incidence and Screening (28 papers), Digital Radiography and Breast Imaging (27 papers) and AI in cancer detection (23 papers). Mary Rickard collaborates with scholars based in Australia, United States and Nigeria. Mary Rickard's co-authors include Ann Poulos, Patrick Brennan, Donald McLean, Ramachandran Chandrasekhar, Y. Attikiouzel, Richard Taylor, Robert Heard, Roger Bourne, Andrew Field and Andrew C. Page and has published in prestigious journals such as SHILAP Revista de lepidopterología, Blood and Psychological Medicine.

In The Last Decade

Mary Rickard

54 papers receiving 848 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mary Rickard Australia 17 378 359 350 253 196 60 882
Rosalind Given-Wilson United Kingdom 20 369 1.0× 447 1.2× 332 0.9× 362 1.4× 159 0.8× 64 1.1k
Sofie Sebuødegård Norway 17 484 1.3× 401 1.1× 457 1.3× 261 1.0× 240 1.2× 34 907
Ruth Walsh United States 18 276 0.7× 647 1.8× 388 1.1× 642 2.5× 221 1.1× 43 1.3k
Paula B. Gordon Canada 15 358 0.9× 216 0.6× 188 0.5× 254 1.0× 317 1.6× 39 944
Karin Leifland Sweden 19 383 1.0× 413 1.2× 437 1.2× 292 1.2× 273 1.4× 30 981
Sujata V. Ghate United States 17 137 0.4× 402 1.1× 253 0.7× 521 2.1× 136 0.7× 38 858
Cherie M. Kuzmiak United States 19 278 0.7× 311 0.9× 337 1.0× 495 2.0× 179 0.9× 83 1.1k
Ellen Shaw de Paredes United States 15 218 0.6× 202 0.6× 130 0.4× 189 0.7× 232 1.2× 30 719
Dag Pavić United States 15 175 0.5× 151 0.4× 170 0.5× 164 0.6× 129 0.7× 31 575
Todd E. Wilson United States 15 205 0.5× 203 0.6× 158 0.5× 180 0.7× 146 0.7× 20 651

Countries citing papers authored by Mary Rickard

Since Specialization
Citations

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

Fields of papers citing papers by Mary Rickard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mary Rickard

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

All Works

20 of 20 papers shown
1.
Gandomkar, Ziba, et al.. (2025). Personalising education for radiologists using AI: a breast imaging case study. SHILAP Revista de lepidopterología. 3. 100166–100166.
2.
Al‐Mousa, Dana S., et al.. (2020). Impact of digital breast tomosynthesis on readers with different experience. 78–78. 1 indexed citations
3.
Rickard, Mary, et al.. (2020). Projecting thyroid cancer risk to the general public from radiation exposure following hypothetical severe nuclear accidents in Canada. Journal of Radiological Protection. 40(4). 1091–1110. 3 indexed citations
4.
Tapia, Kriscia, et al.. (2019). Breast screening attendance of Aboriginal and Torres Strait Islander women in the Northern Territory of Australia. Australian and New Zealand Journal of Public Health. 43(4). 334–339. 7 indexed citations
5.
Ekpo, Ernest, Claudia Mello‐Thoms, Mary Rickard, Patrick Brennan, & Mark F. McEntee. (2016). Breast density (BD) assessment with digital breast tomosynthesis (DBT): Agreement between Quantra™ and 5th edition BI-RADS®. The Breast. 30. 185–190. 17 indexed citations
6.
Mello‐Thoms, Claudia, et al.. (2016). RELATIONSHIP BETWEEN RADIATION DOSE AND IMAGE QUALITY IN DIGITAL BREAST TOMOSYNTHESIS. Radiation Protection Dosimetry. 173(4). ncw005–ncw005. 1 indexed citations
7.
Brennan, Patrick, Claudia Mello‐Thoms, Peter Kench, et al.. (2015). Precision imaging—its impact on image quality and diagnostic confidence in breast ultrasound examinations. British Journal of Radiology. 88(1054). 20140340–20140340. 1 indexed citations
8.
Brennan, Patrick, et al.. (2014). Effect of radiologists’ experience on breast cancer detection and localization using digital breast tomosynthesis. European Radiology. 25(2). 402–409. 17 indexed citations
9.
Bourne, Roger, et al.. (2013). Digital tomosynthesis: A new future for breast imaging?. Clinical Radiology. 68(5). e225–e236. 60 indexed citations
10.
Ryan, Elaine, et al.. (2011). A comparison between the electronic magnification (EM) and true magnification (TM) of breast phantom images using a CDMAM phantom. European Journal of Radiology. 81(7). 1514–1519. 4 indexed citations
11.
Reed, Warren, et al.. (2009). Reader practice in mammography screen reporting in Australia. Journal of Medical Imaging and Radiation Oncology. 53(6). 530–537. 8 indexed citations
12.
Chiu, Clayton, et al.. (2006). Population-based Mammography Screening and Breast Cancer Incidence in New South Wales, Australia. Cancer Causes & Control. 17(2). 153–160. 16 indexed citations
13.
Nguyen, Hung T., et al.. (2005). Mammogram object detection using dendronic image analysis. PubMed. 3. 1763–1765.
14.
Rickard, Mary, Richard Taylor, Andrew C. Page, & Jane Estoesta. (2005). Cancer detection and mammogram volume of radiologists in a population-based screening programme. The Breast. 15(1). 39–43. 13 indexed citations
15.
Chandrasekhar, Ramachandran, et al.. (2004). Automatic Pectoral Muscle Segmentation on Mediolateral Oblique View Mammograms. IEEE Transactions on Medical Imaging. 23(9). 1129–1140. 108 indexed citations
16.
Poulos, Ann, Donald McLean, Mary Rickard, & Robert Heard. (2003). Breast compression in mammography: How much is enough?. Australasian Radiology. 47(2). 121–126. 62 indexed citations
17.
Nguyen, Hung T., et al.. (2003). Diagnostic abilities of three CAD methods for assessing microcalcifications in mammograms and an aspect of equivocal cases decisions by radiologists. Australasian Physical & Engineering Sciences in Medicine. 26(3). 104–109. 1 indexed citations
18.
Crawford, Michael D., et al.. (2000). THE OPERATIVE MANAGEMENT OF SCREEN‐DETECTED BREAST CANCERS. Australian and New Zealand Journal of Surgery. 70(3). 168–173. 5 indexed citations
19.
O׳Donnell, Maryanne, Richard Fisher, Kathryn M. Irvine, Mary Rickard, & N. McConaghy. (2000). Emotional suppression: can it predict cancer outcome in women with suspicious screening mammograms?. Psychological Medicine. 30(5). 1079–1088. 12 indexed citations
20.
Rickard, Mary. (1984). The Role of Computed Tomography and Other Imaging Modalities in Malignant Lymphoma. Australasian Radiology. 28(2). 140–147. 1 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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