Joseph N. Stember

555 total citations
31 papers, 340 citations indexed

About

Joseph N. Stember is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Joseph N. Stember has authored 31 papers receiving a total of 340 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Radiology, Nuclear Medicine and Imaging, 11 papers in Pulmonary and Respiratory Medicine and 7 papers in Artificial Intelligence. Recurrent topics in Joseph N. Stember's work include Radiomics and Machine Learning in Medical Imaging (11 papers), MRI in cancer diagnosis (4 papers) and AI in cancer detection (4 papers). Joseph N. Stember is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (11 papers), MRI in cancer diagnosis (4 papers) and AI in cancer detection (4 papers). Joseph N. Stember collaborates with scholars based in United States, India and Malaysia. Joseph N. Stember's co-authors include Willy Wriggers, Peter Chang, Sachin Jambawalikar, Jane P. Ko, David P. Naidich, Jack Grinband, Michael Z. Liu, Christopher G. Filippi, Philip M. Meyers and Bradford J. Wood and has published in prestigious journals such as The Journal of Chemical Physics, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Joseph N. Stember

29 papers receiving 336 citations

Peers

Joseph N. Stember
Jinhao Lyu China
Mojtaba Shamsaei Iran
Caohui Duan China
Mitsutaka Nemoto Japan
Daniel Maleike Germany
Spencer Lewis United States
Baixuan Xu China
Tim Jerman Slovenia
Hongxia Yin China
Liliana Caldeira Germany
Jinhao Lyu China
Joseph N. Stember
Citations per year, relative to Joseph N. Stember Joseph N. Stember (= 1×) peers Jinhao Lyu

Countries citing papers authored by Joseph N. Stember

Since Specialization
Citations

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

Fields of papers citing papers by Joseph N. Stember

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joseph N. Stember. 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 Joseph N. Stember. The network helps show where Joseph N. Stember may publish in the future.

Co-authorship network of co-authors of Joseph N. Stember

This figure shows the co-authorship network connecting the top 25 collaborators of Joseph N. Stember. A scholar is included among the top collaborators of Joseph N. Stember 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 Joseph N. Stember. Joseph N. Stember 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.
Hatzoglou, Vaios, et al.. (2025). Automated Midline Shift Detection in Head CT Using Localization and Symmetry Techniques Based on User-Selected Slice. American Journal of Neuroradiology. 46(9). 1859–1867. 1 indexed citations
2.
Kline, Timothy L., Felipe Kitamura, Ian Pan, et al.. (2025). Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: Classification. Journal of Imaging Informatics in Medicine. 2 indexed citations
3.
Stember, Joseph N., Luca Pasquini, Kyung K. Peck, et al.. (2024). Evolutionary Strategies Enable Systematic and Reliable Uncertainty Quantification: A Proof-of-Concept Pilot Study on Resting-State Functional MRI Language Lateralization. Journal of Imaging Informatics in Medicine. 38(1). 576–586.
4.
Swinburne, Nathaniel, Christopher Jackson, Andrew Pagano, et al.. (2024). Foundational Segmentation Models and Clinical Data Mining Enable Accurate Computer Vision for Lung Cancer. Journal of Imaging Informatics in Medicine. 38(3). 1552–1562. 1 indexed citations
5.
Gutman, David A., Andrei I. Holodny, Shakeel Modak, et al.. (2024). Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection. Journal of Imaging Informatics in Medicine. 37(6). 2920–2930. 1 indexed citations
6.
Bhowmik, Arka, Maxine S. Jochelson, Andrei I. Holodny, et al.. (2023). A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research. Journal of Magnetic Resonance Imaging. 59(2). 450–480. 9 indexed citations
7.
Stember, Joseph N., et al.. (2022). Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution. Journal of Digital Imaging. 36(2). 536–546. 6 indexed citations
8.
Stember, Joseph N., et al.. (2022). Deep Reinforcement Learning with Automated Label Extraction from Clinical Reports Accurately Classifies 3D MRI Brain Volumes. Journal of Digital Imaging. 35(5). 1143–1152. 14 indexed citations
9.
Eskreis‐Winkler, Sarah, Elizabeth J. Sutton, Donna D’Alessio, et al.. (2022). Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist. Journal of Magnetic Resonance Imaging. 56(4). 1068–1076. 17 indexed citations
10.
Stember, Joseph N., et al.. (2021). Panoramic Dental Reconstruction for Faster Detection of Dental Pathology on Medical Non-dental CT Scans: a Proof of Concept from CT Neck Soft Tissue. Journal of Digital Imaging. 34(4). 959–966. 5 indexed citations
11.
12.
Stember, Joseph N., Arif Sheikh, Chaitanya Divgi, et al.. (2019). A threshold-based method to predict thyroid nodules on scintigraphy scans. Biomedical Physics & Engineering Express. 6(1). 15019–15019. 1 indexed citations
13.
Stember, Joseph N.. (2018). Three-Dimensional Surface Point Cloud Ultrasound for Better Understanding and Transmission of Ultrasound Scan Information. Journal of Digital Imaging. 31(6). 904–911. 3 indexed citations
14.
Stember, Joseph N., Peter Chang, Michael Z. Liu, et al.. (2018). Convolutional Neural Networks for the Detection and Measurement of Cerebral Aneurysms on Magnetic Resonance Angiography. Journal of Digital Imaging. 32(5). 808–815. 75 indexed citations
15.
Stember, Joseph N., et al.. (2016). Sonographic Prediction of Pediatric Renal Scarring With Full Parameter Normalization. Journal of Ultrasound in Medicine. 35(8). 1639–1643. 1 indexed citations
16.
Stember, Joseph N.. (2014). The Normal Mode Analysis Shape Detection Method for Automated Shape Determination of Lung Nodules. Journal of Digital Imaging. 28(2). 224–230. 4 indexed citations
17.
Stember, Joseph N.. (2013). A Case of Colorectal Cancer with Metastasis to the Chest Wall and Subsequent Hematoma Formation. Journal of Radiology Case Reports. 7(1). 18–24. 1 indexed citations
18.
Stember, Joseph N., Jane P. Ko, David P. Naidich, Manmeen Kaur, & Henry Rusinek. (2012). The Self-Overlap Method for Assessment of Lung Nodule Morphology in Chest CT. Journal of Digital Imaging. 26(2). 239–247. 5 indexed citations
19.
Ko, Jane P., et al.. (2011). Dual-energy Computed Tomography. Journal of Thoracic Imaging. 27(1). 7–22. 47 indexed citations
20.
Stember, Joseph N. & Olaf S. Andersen. (2011). A One-Dimensional Continuum Elastic Model for Membrane-Embedded Gramicidin Dimer Dissociation. PLoS ONE. 6(2). e15563–e15563. 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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