Saima Rathore

2.9k total citations · 1 hit paper
54 papers, 1.4k citations indexed

About

Saima Rathore is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Artificial Intelligence. According to data from OpenAlex, Saima Rathore has authored 54 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Radiology, Nuclear Medicine and Imaging, 24 papers in Genetics and 13 papers in Artificial Intelligence. Recurrent topics in Saima Rathore's work include Radiomics and Machine Learning in Medical Imaging (31 papers), Glioma Diagnosis and Treatment (24 papers) and MRI in cancer diagnosis (10 papers). Saima Rathore is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (31 papers), Glioma Diagnosis and Treatment (24 papers) and MRI in cancer diagnosis (10 papers). Saima Rathore collaborates with scholars based in United States, Pakistan and South Korea. Saima Rathore's co-authors include Christos Davatzikos, Muhammad Aksam Iftikhar, Mohamad Habes, Hamed Akbari, Jimit Doshi, Spyridon Bakas, Asifullah Khan, Mutawarra Hussain, Martin Rozycki and Lal Hussain and has published in prestigious journals such as Journal of Clinical Oncology, NeuroImage and Cancer.

In The Last Decade

Saima Rathore

50 papers receiving 1.4k 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 on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

2017 420 citations Saima Rathore, Mohamad Habes et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Saima Rathore United States	19	796	416	362	324	217	54	1.4k
Xiao Da United States	19	558 0.7×	466 1.1×	351 1.0×	185 0.6×	312 1.4×	51	1.7k
Yu‐Chuan Hu China	18	779 1.0×	124 0.3×	396 1.1×	263 0.8×	54 0.2×	32	1.2k
Junfeng Lu China	24	579 0.7×	177 0.4×	420 1.2×	175 0.5×	59 0.3×	80	1.6k
Hai‐Yan Nan China	13	468 0.6×	92 0.2×	290 0.8×	223 0.7×	44 0.2×	26	837
Manuel Gómez-Río Spain	19	264 0.3×	137 0.3×	73 0.2×	247 0.8×	221 1.0×	56	1.2k
Francesca Gallivanone Italy	21	947 1.2×	237 0.6×	32 0.1×	120 0.4×	240 1.1×	51	1.9k
Marco Lorenzi France	22	389 0.5×	168 0.4×	39 0.1×	111 0.3×	355 1.6×	93	1.6k
Ioannis Kalatzis Greece	19	356 0.4×	294 0.7×	59 0.2×	131 0.4×	13 0.1×	73	946
Mikhail Milchenko United States	10	437 0.5×	467 1.1×	62 0.2×	90 0.3×	19 0.1×	23	1.1k
Mohammed Goryawala United States	17	339 0.4×	49 0.1×	99 0.3×	83 0.3×	130 0.6×	51	785

Countries citing papers authored by Saima Rathore

Since Specialization

Citations

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

Fields of papers citing papers by Saima Rathore

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saima Rathore

This figure shows the co-authorship network connecting the top 25 collaborators of Saima Rathore. A scholar is included among the top collaborators of Saima Rathore 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 Saima Rathore. Saima Rathore 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

Khan, Asifullah, Zunaira Rauf, Abdul Rehman Khan, et al.. (2025). A Recent Survey of Vision Transformers for Medical Image Segmentation. IEEE Access. 13. 191824–191849. 1 indexed citations

Malpetti, Maura, Saima Rathore, Renaud La Joie, et al.. (2025). Regional tau PET patterns predict prospective domain-specific cognitive decline in early symptomatic Alzheimer’s disease. Alzheimer s Research & Therapy. 17(1). 220–220.

Starr, Alexander, Saima Rathore, Marzieh Daniali, et al.. (2025). Differential Effects of Cannabinoid Receptor 2 Agonists on HIV Replication and Inflammatory Activation in Monocyte-Derived Macrophages and Induced Pluripotent Stem Cell-Derived Microglia. Journal of Neuroimmune Pharmacology. 20(1). 87–87.

Rathore, Saima, Ixavier A. Higgins, Jian Wang, et al.. (2024). Predicting regional tau accumulation with machine learning‐based tau‐PET and advanced radiomics. Alzheimer s & Dementia Translational Research & Clinical Interventions. 10(4). e70005–e70005.

Bathla, Girish, Neetu Soni, Ian T. Mark, et al.. (2024). Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms. American Journal of Neuroradiology. 45(9). 1291–1298. 1 indexed citations

Rathore, Saima, et al.. (2024). Genome‐wide mediation analysis reveals amyloid and tau imaging genetics patterns in Alzheimer’s Disease. Alzheimer s & Dementia. 20(S2).

Bathla, Girish, Neetu Soni, Nicholas B. Larson, et al.. (2023). AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods. Journal of Neuroradiology. 51(3). 258–264. 19 indexed citations

Wang, Jian, et al.. (2023). Bayesian network analysis of BIN1 risk allele and other risk factors and biomarkers of Alzheimer’s disease. Alzheimer s & Dementia. 19(S12). 1 indexed citations

Bakas, Spyridon, Gaurav Shukla, Hamed Akbari, et al.. (2020). Integrative radiomic analysis for pre-surgical prognostic stratification of glioblastoma patients: from advanced to basic MRI protocols. PubMed. 11315. 3 indexed citations

10.

Hussain, Lal, Saima Rathore, Adeel Abbasi, & Sharjil Saeed. (2019). Automated lung cancer detection based on multimodal features extracting strategy using machine learning techniques. 134–134. 18 indexed citations

11.

Davatzikos, Christos, Aristeidis Sotiras, Yong Fan, et al.. (2019). Precision diagnostics based on machine learning-derived imaging signatures. Magnetic Resonance Imaging. 64. 49–61. 20 indexed citations

12.

Rathore, Saima, Hamed Akbari, Spyridon Bakas, et al.. (2019). Multivariate Analysis of Preoperative Magnetic Resonance Imaging Reveals Transcriptomic Classification of de novo Glioblastoma Patients. Frontiers in Computational Neuroscience. 13. 81–81. 7 indexed citations

13.

Rathore, Saima, Hamed Akbari, Martin Rozycki, et al.. (2018). Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1. Scientific Reports. 8(1). 5087–5087. 100 indexed citations

14.

Asim, Yousra, et al.. (2018). Community-Centric Brokerage-Aware Access Control for Online Social Networks. Future Generation Computer Systems. 109. 469–478. 7 indexed citations

15.

Bakas, Spyridon, Hamed Akbari, Jared Pisapia, et al.. (2017). In Vivo Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature Consistent with Deep Peritumoral Infiltration: The ϕ -Index. Clinical Cancer Research. 23(16). 4724–4734. 63 indexed citations

16.

Pisapia, Jared, Hamed Akbari, Martin Rozycki, et al.. (2017). Use of Fetal Magnetic Resonance Image Analysis and Machine Learning to Predict the Need for Postnatal Cerebrospinal Fluid Diversion in Fetal Ventriculomegaly. JAMA Pediatrics. 172(2). 128–128. 19 indexed citations

17.

Rathore, Saima, Hamed Akbari, Martin Rozycki, et al.. (2017). NIMG-59. RADIOLOGIC SUBTYPES OF GLIOBLASTOMA CALCULATED VIA MULTI-PARAMETRIC IMAGING SIGNATURES REVEAL COMPLEMENTARY INFORMATION TO CURRENT WHO CLASSIFICATION. Neuro-Oncology. 19(suppl_6). vi155–vi156. 3 indexed citations

18.

Zeng, Ke, Spyridon Bakas, Aristeidis Sotiras, et al.. (2016). Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework. Lecture notes in computer science. 10154. 184–194. 23 indexed citations

19.

Rathore, Saima, Mutawarra Hussain, & Asifullah Khan. (2015). Automated colon cancer detection using hybrid of novel geometric features and some traditional features. Computers in Biology and Medicine. 65. 279–296. 71 indexed citations

20.

Rathore, Saima, et al.. (2012). Parameter optimization for non-local de-noising using Elite GA. 105. 194–199. 6 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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