Michelle Bardis

853 total citations · 1 hit paper
12 papers, 576 citations indexed

About

Michelle Bardis is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Michelle Bardis has authored 12 papers receiving a total of 576 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Pulmonary and Respiratory Medicine and 5 papers in Biomedical Engineering. Recurrent topics in Michelle Bardis's work include Radiomics and Machine Learning in Medical Imaging (8 papers), Prostate Cancer Diagnosis and Treatment (5 papers) and Medical Imaging and Analysis (3 papers). Michelle Bardis is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (8 papers), Prostate Cancer Diagnosis and Treatment (5 papers) and Medical Imaging and Analysis (3 papers). Michelle Bardis collaborates with scholars based in United States, India and Canada. Michelle Bardis's co-authors include Daniel Chow, Christopher G. Filippi, Peter Chang, Brent D. Weinberg, Jack Grinband, Daniela A. Bota, Soonmee Cha, Min‐Ying Su, Pierre Baldi and Laila Poisson and has published in prestigious journals such as Journal of Clinical Oncology, Stroke and American Journal of Roentgenology.

In The Last Decade

Michelle Bardis

12 papers receiving 561 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.

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas

2018 325 citations Peter Chang, Jack Grinband et al. American Journal of Neuroradiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michelle Bardis United States	8	370	178	151	132	104	12	576
Hwan-ho Cho South Korea	15	683 1.8×	174 1.0×	219 1.5×	163 1.2×	97 0.9×	29	819
Qihua Li China	6	678 1.8×	268 1.5×	169 1.1×	166 1.3×	86 0.8×	12	812
Qiuchang Sun China	11	649 1.8×	258 1.4×	198 1.3×	177 1.3×	79 0.8×	15	755
Anahita Fathi Kazerooni United States	16	396 1.1×	209 1.2×	58 0.4×	48 0.4×	85 0.8×	65	606
Sebastian R. van der Voort Netherlands	12	369 1.0×	212 1.2×	184 1.2×	43 0.3×	80 0.8×	28	542
Khashayar Namdar Canada	10	280 0.8×	86 0.5×	109 0.7×	81 0.6×	52 0.5×	26	470
Ying‐Zhi Sun China	10	446 1.2×	327 1.8×	72 0.5×	55 0.4×	88 0.8×	10	532
Ramón Correa United States	11	372 1.0×	198 1.1×	110 0.7×	93 0.7×	42 0.4×	30	526
Brian Hrycushko United States	15	406 1.1×	46 0.3×	240 1.6×	64 0.5×	68 0.7×	54	742
Xiaopan Xu China	16	617 1.7×	130 0.7×	190 1.3×	70 0.5×	35 0.3×	30	832

Countries citing papers authored by Michelle Bardis

Since Specialization

Citations

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

Fields of papers citing papers by Michelle Bardis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michelle Bardis

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

All Works

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12 of 12 papers shown

1.

Houshyar, Roozbeh, Justin Glavis‐Bloom, Thanh‐Lan Bui, et al.. (2021). Outcomes of Artificial Intelligence Volumetric Assessment of Kidneys and Renal Tumors for Preoperative Assessment of Nephron-Sparing Interventions. Journal of Endourology. 35(9). 1411–1418. 11 indexed citations

2.

Bardis, Michelle, et al.. (2021). Segmentation of the Prostate Transition Zone and Peripheral Zone on MR Images with Deep Learning. Radiology Imaging Cancer. 3(3). e200024–e200024. 42 indexed citations

3.

Callen, Andrew L., Daniel Chow, Michelle Bardis, et al.. (2020). Predictive Value of Noncontrast Head CT with Negative Findings in the Emergency Department Setting. American Journal of Neuroradiology. 41(2). 213–218. 2 indexed citations

4.

Bardis, Michelle, Roozbeh Houshyar, Justin Glavis‐Bloom, et al.. (2020). Deep Learning with Limited Data: Organ Segmentation Performance by U-Net. Electronics. 9(8). 1199–1199. 25 indexed citations

5.

Bardis, Michelle, Justin Glavis‐Bloom, Edward Uchio, et al.. (2020). A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI. American Journal of Roentgenology. 216(1). 111–116. 47 indexed citations

6.

Yu, Wengui, Michelle Bardis, Min‐Ying Su, et al.. (2020). Abstract WP395: Detection of Hemorrhagic Expansion With Ai. Stroke. 51(Suppl_1). 2 indexed citations

7.

Bardis, Michelle, Roozbeh Houshyar, Peter Chang, et al.. (2020). Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends. Cancers. 12(5). 1204–1204. 43 indexed citations

8.

Bardis, Michelle, Jeremy Martin, Justin Glavis‐Bloom, et al.. (2019). Deep learning segmentation of kidneys with renal cell carcinoma.. Journal of Clinical Oncology. 37(15_suppl). e16098–e16098. 9 indexed citations

9.

Bardis, Michelle, et al.. (2019). Automated prostate lesion detection and PI-RADS assessment with deep learning.. Journal of Clinical Oncology. 37(15_suppl). e16605–e16605. 2 indexed citations

10.

Bardis, Michelle, Daniela A. Bota, Christopher G. Filippi, et al.. (2019). Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging. Cancers. 11(6). 829–829. 67 indexed citations

11.

Glavis‐Bloom, Justin, et al.. (2019). Deep learning hybrid 3D/2D convolutional neural network for prostate MRI recognition.. Journal of Clinical Oncology. 37(15_suppl). e16600–e16600. 1 indexed citations

12.

Chang, Peter, Jack Grinband, Brent D. Weinberg, et al.. (2018). Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas. American Journal of Neuroradiology. 39(7). 1201–1207. 325 indexed citations breakdown →

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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