Aly A. Mohamed

734 total citations
11 papers, 510 citations indexed

About

Aly A. Mohamed is a scholar working on Artificial Intelligence, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Aly A. Mohamed has authored 11 papers receiving a total of 510 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Oncology and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Aly A. Mohamed's work include AI in cancer detection (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Global Cancer Incidence and Screening (5 papers). Aly A. Mohamed is often cited by papers focused on AI in cancer detection (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Global Cancer Incidence and Screening (5 papers). Aly A. Mohamed collaborates with scholars based in United States and China. Aly A. Mohamed's co-authors include Shandong Wu, Wendie A. Berg, Yahong Luo, Rachel C. Jankowitz, Jules H. Sumkin, Hong Peng, Margarita L. Zuley, Dooman Arefan, Lei Zhang and Ruimei Chai and has published in prestigious journals such as Clinical Cancer Research, Medical Physics and Journal of Magnetic Resonance Imaging.

In The Last Decade

Aly A. Mohamed

11 papers receiving 493 citations

Peers

Aly A. Mohamed

AI

RNMI

PRM

ONCOL

CVPR

Gopichandh Danala United States

Seyedehnafiseh Mirniaharikandehei United States

Rikke Rass Winkel Denmark

Caleb Richter United States

Christoph Haarburger Germany

Eleftherios Trivizakis Greece

Alexandra Edwards United States

Gustav Müller‐Franzes Germany

Yan‐Wei Lee Taiwan

Paul Desbordes France

Gopichandh Danala United States

Aly A. Mohamed

342 ×0.8

AI

447 ×1.3

RNMI

123 ×0.8

PRM

54 ×0.8

ONCOL

88 ×1.4

CVPR

Citations per year, relative to Aly A. Mohamed Aly A. Mohamed (= 1×) peers Gopichandh Danala

Countries citing papers authored by Aly A. Mohamed

Since Specialization

Citations

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

Fields of papers citing papers by Aly A. Mohamed

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aly A. Mohamed. 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 Aly A. Mohamed. The network helps show where Aly A. Mohamed may publish in the future.

Co-authorship network of co-authors of Aly A. Mohamed

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

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Mohamed, Aly A., et al.. (2020). Deep Learning Pre-training Strategy for Mammogram Image Classification: an Evaluation Study. Journal of Digital Imaging. 33(5). 1257–1265. 18 indexed citations

2.

Zhang, Lei, et al.. (2020). Inaccurate Labels in Weakly-Supervised Deep Learning: Automatic Identification and Correction and Their Impact on Classification Performance. IEEE Journal of Biomedical and Health Informatics. 24(9). 2701–2710. 31 indexed citations

3.

Mohamed, Aly A., et al.. (2019). Deep learning of sub-regional breast parenchyma in mammograms for localized breast cancer risk prediction. 351. 97–97. 3 indexed citations

4.

Wu, Shandong, et al.. (2019). Deep learning for identifying breast cancer malignancy and false recalls: a robustness study on training strategy. 4–4. 4 indexed citations

5.

Zhang, Lei, et al.. (2019). Automated deep-learning method for whole-breast segmentation in diffusion-weighted breast MRI. 10133. 99–99. 6 indexed citations

6.

Zhang, Lei, Aly A. Mohamed, Ruimei Chai, et al.. (2019). Automated deep learning method for whole‐breast segmentation in diffusion‐weighted breast MRI. Journal of Magnetic Resonance Imaging. 51(2). 635–643. 33 indexed citations

7.

Arefan, Dooman, Aly A. Mohamed, Wendie A. Berg, et al.. (2019). Deep learning modeling using normal mammograms for predicting breast cancer risk. Medical Physics. 47(1). 110–118. 88 indexed citations

8.

Mohamed, Aly A., et al.. (2018). Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening. Clinical Cancer Research. 24(23). 5902–5909. 96 indexed citations

9.

Mohamed, Aly A., et al.. (2018). Do pre-trained deep learning models improve computer-aided classification of digital mammograms?. 6. 74–74. 2 indexed citations

10.

Mohamed, Aly A., Yahong Luo, Hong Peng, Rachel C. Jankowitz, & Shandong Wu. (2017). Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective. Journal of Digital Imaging. 31(4). 387–392. 41 indexed citations

11.

Mohamed, Aly A., Wendie A. Berg, Hong Peng, et al.. (2017). A deep learning method for classifying mammographic breast density categories. Medical Physics. 45(1). 314–321. 188 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.

Explore authors with similar magnitude of impact