Michelle Han

1.3k total citations
21 papers, 642 citations indexed

About

Michelle Han is a scholar working on Radiology, Nuclear Medicine and Imaging, Surgery and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, Michelle Han has authored 21 papers receiving a total of 642 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Surgery and 4 papers in Pediatrics, Perinatology and Child Health. Recurrent topics in Michelle Han's work include Advanced Neuroimaging Techniques and Applications (5 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Neonatal and fetal brain pathology (2 papers). Michelle Han is often cited by papers focused on Advanced Neuroimaging Techniques and Applications (5 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Neonatal and fetal brain pathology (2 papers). Michelle Han collaborates with scholars based in United States, Canada and United Kingdom. Michelle Han's co-authors include Jonathan O’Muircheartaigh, Katie Lehman, Sean Deoni, Nicole Waskiewicz, Douglas Dean, Holly Dirks, Lindsay Walker, Irene Piryatinsky, John D. E. Gabrieli and Zhenghan Qi and has published in prestigious journals such as NeuroImage, Neurology and Diabetes.

In The Last Decade

Michelle Han

20 papers receiving 633 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michelle Han United States 11 225 188 180 106 101 21 642
Katie Lehman United Kingdom 7 327 1.5× 245 1.3× 206 1.1× 125 1.2× 107 1.1× 7 821
Manuel Blesa United Kingdom 16 359 1.6× 187 1.0× 163 0.9× 63 0.6× 75 0.7× 26 646
Sarah Sparrow United Kingdom 12 325 1.4× 101 0.5× 69 0.4× 60 0.6× 114 1.1× 16 503
Jasmine L. Hect United States 16 409 1.8× 167 0.9× 297 1.6× 36 0.3× 31 0.3× 39 847
Henrica M. A. de Bie Netherlands 8 217 1.0× 125 0.7× 349 1.9× 26 0.2× 27 0.3× 10 694
María J. Miranda Denmark 14 301 1.3× 271 1.4× 150 0.8× 20 0.2× 29 0.3× 42 794
Nelly Padilla Sweden 21 1.2k 5.3× 213 1.1× 237 1.3× 51 0.5× 67 0.7× 58 1.5k
M. Benders Netherlands 7 493 2.2× 199 1.1× 125 0.7× 14 0.1× 46 0.5× 13 629
Claire E. Kelly Australia 23 772 3.4× 326 1.7× 211 1.2× 31 0.3× 71 0.7× 51 1.4k
Sarah Treit Canada 16 567 2.5× 405 2.2× 337 1.9× 11 0.1× 57 0.6× 30 1.1k

Countries citing papers authored by Michelle Han

Since Specialization
Citations

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

Fields of papers citing papers by Michelle Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michelle Han

This figure shows the co-authorship network connecting the top 25 collaborators of Michelle Han. A scholar is included among the top collaborators of Michelle Han 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 Han. Michelle Han 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.
Zhang, Michael, Lydia Tam, Jason N. Wright, et al.. (2022). Radiomics Can Distinguish Pediatric Supratentorial Embryonal Tumors, High-Grade Gliomas, and Ependymomas. American Journal of Neuroradiology. 43(4). 603–610. 13 indexed citations
2.
Santoro, Jonathan D., Peter K. Moon, Michelle Han, et al.. (2022). Early Onset Diffusion Abnormalities in Refractory Headache Disorders. Frontiers in Neurology. 13. 898219–898219. 2 indexed citations
3.
Zhang, Michael, Michelle Han, Alireza Radmanesh, et al.. (2021). Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma. American Journal of Neuroradiology. 42(9). 1702–1708. 14 indexed citations
4.
Quon, Jennifer L., Michelle Han, Lily H. Kim, et al.. (2020). Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus. Journal of Neurosurgery Pediatrics. 27(2). 131–138. 38 indexed citations
5.
Han, Michelle, Jennifer L. Quon, Katie Shpanskaya, et al.. (2019). One Hundred Years of Innovation: Automatic Detection of Brain Ventricular Volume using Deep Learning in a Large-Scale Multi-Institutional Study (P5.6-022). Neurology. 92(15_supplement). 1 indexed citations
6.
Tan, Hiangkiat, et al.. (2019). Effectiveness, treatment durability, and treatment costs of canagliflozin and glucagon-like peptide-1 receptor agonists in patients with type 2 diabetes in the USA. BMJ Open Diabetes Research & Care. 7(1). e000704–e000704. 6 indexed citations
7.
Shpanskaya, Katie, Michelle Han, Edward H. Lee, et al.. (2019). Deep Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs. Journal of Vascular and Interventional Radiology. 31(1). 66–73. 10 indexed citations
8.
Han, Michelle, Lily H. Kim, Christine Kim, et al.. (2019). Altered cerebral perfusion in children with Langerhans cell histiocytosis after chemotherapy. Pediatric Blood & Cancer. 67(3). e28104–e28104. 1 indexed citations
9.
Qi, Zhenghan, Michelle Han, Yunxin Wang, et al.. (2019). Speech processing and plasticity in the right hemisphere predict variation in adult foreign language learning. NeuroImage. 192. 76–87. 33 indexed citations
10.
Han, Michelle & Seema A. Khan. (2018). Clinical Trials for Ductal Carcinoma In Situ of the Breast. Journal of Mammary Gland Biology and Neoplasia. 23(4). 293–301. 11 indexed citations
11.
12.
Han, Michelle, et al.. (2018). Late diagnosis of a rare urea cycle disorder mimicking Kleine-Levin syndrome. Neurology Clinical Practice. 8(6). e43–e45.
13.
Hung, Yuwen, Zeynep M. Saygin, Joseph Biederman, et al.. (2016). Impaired Frontal-Limbic White Matter Maturation in Children at Risk for Major Depression. Cerebral Cortex. 27(9). 4478–4491. 19 indexed citations
14.
Lu, Chunming, Zhenghan Qi, Adrianne Harris, et al.. (2015). Shared Neuroanatomical Substrates of Impaired Phonological Working Memory Across Reading Disability and Autism. Biological Psychiatry Cognitive Neuroscience and Neuroimaging. 1(2). 169–177. 15 indexed citations
15.
Han, Michelle & Jeffrey H. Peters. (2014). Ambulatory Esophageal pH Monitoring. Gastrointestinal Endoscopy Clinics of North America. 24(4). 581–594. 3 indexed citations
16.
17.
Qi, Zhenghan, et al.. (2014). White-matter structure in the right hemisphere predicts Mandarin Chinese learning success. Journal of Neurolinguistics. 33. 14–28. 54 indexed citations
18.
Dean, Douglas, Holly Dirks, Jonathan O’Muircheartaigh, et al.. (2013). Pediatric neuroimaging using magnetic resonance imaging during non-sedated sleep. Pediatric Radiology. 44(1). 64–72. 105 indexed citations
19.
Deoni, Sean, Douglas Dean, Irene Piryatinsky, et al.. (2013). Breastfeeding and early white matter development: A cross-sectional study. NeuroImage. 82. 77–86. 202 indexed citations
20.
Dean, Douglas, Jonathan O’Muircheartaigh, Holly Dirks, et al.. (2013). Modeling healthy male white matter and myelin development: 3 through 60months of age. NeuroImage. 84. 742–752. 104 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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