Dmitry Goldgof

1.3k total citations · 1 hit paper
27 papers, 841 citations indexed

About

Dmitry Goldgof is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Dmitry Goldgof has authored 27 papers receiving a total of 841 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 8 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Dmitry Goldgof's work include Radiomics and Machine Learning in Medical Imaging (7 papers), AI in cancer detection (6 papers) and Cell Image Analysis Techniques (6 papers). Dmitry Goldgof is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (7 papers), AI in cancer detection (6 papers) and Cell Image Analysis Techniques (6 papers). Dmitry Goldgof collaborates with scholars based in United States, Czechia and Saudi Arabia. Dmitry Goldgof's co-authors include Matthew B. Schabath, Robert J. Gillies, Yoganand Balagurunathan, Dylan Hunt, Laurence E. Court, Ghanim Ullah, Mahmoud A. Abdalah, Geoffrey Zhang, Kujtim Latifi and Dennis Mackin and has published in prestigious journals such as Pattern Recognition, Medical Physics and IEEE Transactions on Aerospace and Electronic Systems.

In The Last Decade

Dmitry Goldgof

24 papers receiving 822 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.

Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels

2017 444 citations Yoganand Balagurunathan, Matthew B. Schabath et al. profile →

Peers

Dmitry Goldgof

RNMI

BE

PRM

AI

CVPR

Aria Pezeshk United States

Wenjian Qin China

Ming Chao United States

Duc Fehr United States

Olof Enqvist Sweden

Jue Jiang United States

Matteo Interlenghi Italy

Yang Song China

Jihye Yun South Korea

Ali Gooya United Kingdom

Aria Pezeshk United States

Dmitry Goldgof

654 ×1.1

RNMI

192 ×0.8

BE

264 ×1.1

PRM

296 ×1.8

AI

201 ×1.3

CVPR

Citations per year, relative to Dmitry Goldgof Dmitry Goldgof (= 1×) peers Aria Pezeshk

Countries citing papers authored by Dmitry Goldgof

Since Specialization

Citations

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

Fields of papers citing papers by Dmitry Goldgof

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dmitry Goldgof

This figure shows the co-authorship network connecting the top 25 collaborators of Dmitry Goldgof. A scholar is included among the top collaborators of Dmitry Goldgof 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 Dmitry Goldgof. Dmitry Goldgof 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:

20 of 20 papers shown

1.

Ou, Xinming, et al.. (2024). A Preliminary Study on Using Large Language Models in Software Pentesting. 4 indexed citations

2.

Hossain, Md. Imran, Ghada Zamzmi, Peter R. Mouton, Yu Sun, & Dmitry Goldgof. (2024). Enhancing Concept-Based Explanation with Vision-Language Models. PubMed. 2024. 219–224. 1 indexed citations

3.

Goldgof, Dmitry, et al.. (2023). MIMO U-Net: efficient cell segmentation and counting in microscopy image sequences. 30–30. 3 indexed citations

4.

Hall, Lawrence, et al.. (2023). MIMO YOLO - A Multiple Input Multiple Output Model for Automatic Cell Counting. 827–831. 2 indexed citations

5.

Goldgof, Dmitry, et al.. (2022). Classification of global microglia proliferation based on deep learning with local images. 89–89. 1 indexed citations

6.

Paul, Rahul, Dmitry Cherezov, Matthew B. Schabath, et al.. (2021). Deep radiomics: deep learning on radiomics texture images. 66–66. 4 indexed citations

7.

Paul, Rahul, Matthew B. Schabath, Robert J. Gillies, Lawrence Hall, & Dmitry Goldgof. (2020). Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future. Computers in Biology and Medicine. 122. 103882–103882. 23 indexed citations

8.

Goldgof, Dmitry, et al.. (2019). SQL-Identifier Injection Attacks. 151–159. 1 indexed citations

9.

Phoulady, Hady Ahmady, Dmitry Goldgof, Lawrence Hall, & Peter R. Mouton. (2019). Automatic ground truth for deep learning stereology of immunostained neurons and microglia in mouse neocortex. Journal of Chemical Neuroanatomy. 98. 1–7. 13 indexed citations

10.

Liu, Peiwei, et al.. (2019). A Dual-Task Interference Game-Based Experimental Framework for Comparing the Usability of Authentication Methods. 95–100. 1 indexed citations

11.

Goldgof, Dmitry, et al.. (2019). NOVEL STAIN SEPARATION METHOD FOR AUTOMATIC STEREOLOGY OF IMMUNOSTAINED TISSUE SECTIONS. Innovation in Aging. 3(Supplement_1). S256–S256. 3 indexed citations

12.

Ligatti, Jay, et al.. (2019). Coauthentication. 1906–1915. 1 indexed citations

13.

Cherezov, Dmitry, Samuel Hawkins, Dmitry Goldgof, et al.. (2016). Improving malignancy prediction through feature selection informed by nodule size ranges in NLST. PubMed. 2016. 1939–1944. 5 indexed citations

14.

Paul, Rahul, Samuel Hawkins, Yoganand Balagurunathan, et al.. (2016). Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival among Patients with Lung Adenocarcinoma. Tomography. 2(4). 388–395. 133 indexed citations

15.

Phoulady, Hady Ahmady, Baishali Chaudhury, Dmitry Goldgof, et al.. (2014). Experiments with large ensembles for segmentation and classification of cervical cancer biopsy images. 870–875. 6 indexed citations

16.

Candamo, Joshua, Rangachar Kasturi, Dmitry Goldgof, & Sudeep Sarkar. (2009). Detection of Thin Lines using Low-Quality Video from Low-Altitude Aircraft in Urban Settings. IEEE Transactions on Aerospace and Electronic Systems. 45(3). 937–949. 52 indexed citations

17.

Zhang, Yong, Yan Qiu, Dmitry Goldgof, Sudeep Sarkar, & Lihua Li. (2007). 3D Finite Element Modeling of Nonrigid Breast Deformation for Feature Registration in -ray and MR Images. 38–38. 12 indexed citations

18.

Garofolo, John S., et al.. (2006). PERFORMANCE EVALUATION PROTOCOL FOR FACE, PERSON AND VEHICLE DETECTION & TRACKING IN VIDEO ANALYSIS AND CONTENT EXTRACTION (VACE-II) CLEAR - CLASSIFICATION OF EVENTS, ACTIVITIES AND RELATIONSHIPS. 14 indexed citations

19.

Clarke, Laurence P., Robert P. Velthuizen, Matthew D. Clark, et al.. (1998). MRI Measurement of Brain Tumor Response: Comparison of Visual Metric and Automatic Segmentation. Magnetic Resonance Imaging. 16(3). 271–279. 71 indexed citations

20.

Goldgof, Dmitry, et al.. (1994). Parallel algorithms for circle detection in images. Pattern Recognition. 27(8). 1019–1028. 15 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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