Mateusz Buda

4.3k total citations · 2 hit papers
13 papers, 2.7k citations indexed

About

Mateusz Buda is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Mateusz Buda has authored 13 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 9 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Mateusz Buda's work include Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (8 papers) and Thyroid Cancer Diagnosis and Treatment (4 papers). Mateusz Buda is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (8 papers) and Thyroid Cancer Diagnosis and Treatment (4 papers). Mateusz Buda collaborates with scholars based in United States and Sweden. Mateusz Buda's co-authors include Maciej A. Mazurowski, Atsuto Maki, Ashirbani Saha, Mustafa R. Bashir, Benjamin Wildman‐Tobriner, Jenny K. Hoang, William D. Middleton, Franklin N. Tessler, David Thayer and Ryan G. Short and has published in prestigious journals such as Scientific Reports, Radiology and Neural Networks.

In The Last Decade

Mateusz Buda

13 papers receiving 2.6k 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 systematic study of the class imbalance problem in convolutional neural networks

2018 1.7k citations Mateusz Buda, Atsuto Maki et al. Neural Networks profile →
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

2018 362 citations Maciej A. Mazurowski, Mateusz Buda et al. Journal of Magnetic Resonance Imaging profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mateusz Buda United States	10	1.3k	816	624	228	187	13	2.7k
Yi Guo China	30	757 0.6×	1.5k 1.9×	588 0.9×	360 1.6×	474 2.5×	163	3.5k
Ekta Walia India	19	726 0.6×	716 0.9×	1.3k 2.1×	135 0.6×	225 1.2×	55	2.3k
Tuan D. Pham Australia	33	1.2k 0.9×	950 1.2×	1.2k 1.9×	40 0.2×	303 1.6×	295	4.2k
Rikiya Yamashita United States	18	1.1k 0.9×	1.6k 2.0×	638 1.0×	36 0.2×	499 2.7×	49	4.1k
Mohammed Elmogy Egypt	26	768 0.6×	910 1.1×	817 1.3×	67 0.3×	237 1.3×	169	2.5k
Chiyuan Zhang China	28	1.5k 1.2×	233 0.3×	1.1k 1.7×	154 0.7×	155 0.8×	92	4.8k
J. Shin United States	7	1.1k 0.9×	1.0k 1.3×	848 1.4×	31 0.1×	271 1.4×	7	2.5k
Isabella Nogues United States	4	1.5k 1.2×	1.5k 1.9×	1.1k 1.8×	41 0.2×	460 2.5×	6	4.1k
Cheng Chen China	22	949 0.8×	728 0.9×	662 1.1×	26 0.1×	256 1.4×	87	2.1k
Alessandro Giusti Switzerland	30	1.6k 1.3×	796 1.0×	2.1k 3.4×	55 0.2×	424 2.3×	139	5.2k

Countries citing papers authored by Mateusz Buda

Since Specialization

Citations

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

Fields of papers citing papers by Mateusz Buda

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mateusz Buda

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

13 of 13 papers shown

Wildman‐Tobriner, Benjamin, Mateusz Buda, Jichen Yang, et al.. (2023). Deep learning for classification of thyroid nodules on ultrasound: validation on an independent dataset. Clinical Imaging. 99. 60–66. 4 indexed citations

Konz, Nicholas, et al.. (2021). A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis. Scientific Reports. 11(1). 10276–10276. 14 indexed citations

Buda, Mateusz, Ashirbani Saha, Ruth Walsh, et al.. (2021). A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images. JAMA Network Open. 4(8). e2119100–e2119100. 63 indexed citations

Buda, Mateusz, Ehab A. AlBadawy, Ashirbani Saha, & Maciej A. Mazurowski. (2020). Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images. Radiology Artificial Intelligence. 2(1). e180050–e180050. 16 indexed citations

Buda, Mateusz, et al.. (2020). MRI image harmonization using cycle-consistent generative adversarial network. 36–36. 33 indexed citations

Buda, Mateusz, Ashirbani Saha, Nianyi Li, et al.. (2020). Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images. 3 indexed citations

Said, Nicholas, J. M. O’Donnell, Mateusz Buda, et al.. (2020). Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms. 128–128. 1 indexed citations

Wildman‐Tobriner, Benjamin, Mateusz Buda, Jenny K. Hoang, et al.. (2019). Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility. Radiology. 292(1). 112–119. 103 indexed citations

Buda, Mateusz, Benjamin Wildman‐Tobriner, Jenny K. Hoang, et al.. (2019). Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists. Radiology. 292(3). 695–701. 152 indexed citations

10.

Buda, Mateusz, et al.. (2019). Deep Learning-Based Segmentation of Nodules in Thyroid Ultrasound: Improving Performance by Utilizing Markers Present in the Images. Ultrasound in Medicine & Biology. 46(2). 415–421. 31 indexed citations

11.

Buda, Mateusz, Ashirbani Saha, & Maciej A. Mazurowski. (2019). Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. Computers in Biology and Medicine. 109. 218–225. 199 indexed citations

12.

Mazurowski, Maciej A., Mateusz Buda, Ashirbani Saha, & Mustafa R. Bashir. (2018). Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. Journal of Magnetic Resonance Imaging. 49(4). 939–954. 362 indexed citations breakdown →

13.

Buda, Mateusz, Atsuto Maki, & Maciej A. Mazurowski. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks. 106. 249–259. 1716 indexed citations breakdown →

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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