Nima Tajbakhsh

19.9k total citations · 5 hit papers
27 papers, 12.1k citations indexed

About

Nima Tajbakhsh is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Nima Tajbakhsh has authored 27 papers receiving a total of 12.1k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 12 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Artificial Intelligence. Recurrent topics in Nima Tajbakhsh's work include Radiomics and Machine Learning in Medical Imaging (10 papers), AI in cancer detection (7 papers) and Colorectal Cancer Screening and Detection (7 papers). Nima Tajbakhsh is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (10 papers), AI in cancer detection (7 papers) and Colorectal Cancer Screening and Detection (7 papers). Nima Tajbakhsh collaborates with scholars based in United States, Iran and China. Nima Tajbakhsh's co-authors include Jianming Liang, Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Suryakanth Gurudu, Michael B. Gotway, J. Shin, R. Todd Hurst, Christopher B. Kendall, Xiaowei Ding and Zhihao Wu and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Pattern Recognition and Medical Image Analysis.

In The Last Decade

Nima Tajbakhsh

26 papers receiving 11.9k citations

Hit Papers

UNet++: A Nested U-Net Architecture for Medical Image Seg... 2015 2026 2018 2022 2018 2019 2016 2015 2020 1000 2.0k 3.0k 4.0k 5.0k
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.

  1. UNet++: A Nested U-Net Architecture for Medical Image Segmentation
    2018 5.3k citations Zongwei Zhou, Md Mahfuzur Rahman Siddiquee et al. Lecture notes in computer science profile →
  2. UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
    2019 2.6k citations Zongwei Zhou, Md Mahfuzur Rahman Siddiquee et al. IEEE Transactions on Medical Imaging profile →
  3. Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
    2016 2.1k citations Nima Tajbakhsh, J. Shin et al. IEEE Transactions on Medical Imaging profile →
  4. Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information
    2015 644 citations Nima Tajbakhsh, Suryakanth Gurudu et al. IEEE Transactions on Medical Imaging profile →
  5. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
    2020 568 citations Nima Tajbakhsh, Qian Li et al. Medical Image Analysis profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nima Tajbakhsh United States 19 5.4k 4.8k 4.1k 1.5k 1.3k 27 12.1k
Jianming Liang United States 29 5.5k 1.0× 4.9k 1.0× 4.3k 1.0× 1.5k 1.0× 1.3k 1.0× 89 12.8k
Zongwei Zhou United States 21 4.3k 0.8× 3.5k 0.7× 3.7k 0.9× 1.2k 0.8× 1.0k 0.8× 45 9.9k
Hao Chen China 56 9.7k 1.8× 5.9k 1.2× 6.8k 1.6× 2.1k 1.4× 1.4k 1.0× 354 19.4k
Md Mahfuzur Rahman Siddiquee United States 9 3.9k 0.7× 3.0k 0.6× 2.5k 0.6× 1.1k 0.7× 964 0.7× 22 8.2k
Karthikeyan Shanmugam United States 17 6.2k 1.1× 3.3k 0.7× 3.8k 0.9× 1.3k 0.9× 566 0.4× 72 18.5k
Pheng‐Ann Heng Hong Kong 71 11.5k 2.1× 6.8k 1.4× 7.1k 1.7× 2.9k 2.0× 1.5k 1.2× 535 22.8k
Qi Dou Hong Kong 42 4.4k 0.8× 4.5k 0.9× 4.4k 1.1× 1.9k 1.3× 1.1k 0.9× 174 10.5k
Holger R. Roth United States 34 3.6k 0.7× 4.5k 0.9× 3.8k 0.9× 1.4k 1.0× 1.1k 0.8× 105 9.8k
Le Lü United States 39 3.0k 0.6× 4.0k 0.8× 3.3k 0.8× 1.5k 1.0× 627 0.5× 188 9.8k
Dmitry B. Goldgof United States 46 3.3k 0.6× 4.1k 0.8× 2.4k 0.6× 1.4k 0.9× 756 0.6× 275 9.5k

Countries citing papers authored by Nima Tajbakhsh

Since Specialization
Citations

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

Fields of papers citing papers by Nima Tajbakhsh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nima Tajbakhsh

This figure shows the co-authorship network connecting the top 25 collaborators of Nima Tajbakhsh. A scholar is included among the top collaborators of Nima Tajbakhsh 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 Nima Tajbakhsh. Nima Tajbakhsh 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.
Tajbakhsh, Nima, et al.. (2020). Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis. 63. 101693–101693. 568 indexed citations breakdown →
2.
Tajbakhsh, Nima, et al.. (2020). ErrorNet: Learning Error Representations from Limited Data to Improve Vascular Segmentation. 1364–1368. 9 indexed citations
3.
Tajbakhsh, Nima, J. Shin, Michael B. Gotway, & Jianming Liang. (2019). Computer-aided detection and visualization of pulmonary embolism using a novel, compact, and discriminative image representation. Medical Image Analysis. 58. 101541–101541. 44 indexed citations
4.
5.
Hatamizadeh, Ali, et al.. (2019). Fast and automatic segmentation of pulmonary lobes from chest CT using a progressive dense V-network. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization. 8(5). 509–518. 21 indexed citations
6.
Zhou, Zongwei, Md Mahfuzur Rahman Siddiquee, Ruibin Feng, et al.. (2019). Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis. Lecture notes in computer science. 11767. 384–393. 164 indexed citations
7.
Siddiquee, Md Mahfuzur Rahman, Zongwei Zhou, Nima Tajbakhsh, et al.. (2019). Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization. PubMed. 2019. 191–200. 62 indexed citations
8.
Zhou, Zongwei, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, & Jianming Liang. (2019). UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation. IEEE Transactions on Medical Imaging. 39(6). 1856–1867. 2564 indexed citations breakdown →
9.
Zhou, Zongwei, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, & Jianming Liang. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Lecture notes in computer science. 11045. 3–11. 5276 indexed citations breakdown →
10.
Khokhar, Ashfaq, et al.. (2018). Reduction in training time of a deep learning model in detection of lesions in CT. 21. 139–139. 1 indexed citations
11.
Shin, J., et al.. (2017). Automatic polyp detection in colonoscopy videos. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10133. 101332K–101332K. 23 indexed citations
12.
Tajbakhsh, Nima, J. Shin, Suryakanth Gurudu, et al.. (2016). Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?. IEEE Transactions on Medical Imaging. 35(5). 1299–1312. 2079 indexed citations breakdown →
13.
Shin, J., Nima Tajbakhsh, R. Todd Hurst, Christopher B. Kendall, & Jianming Liang. (2016). Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks. 2526–2535. 46 indexed citations
14.
Tajbakhsh, Nima & Kenji Suzuki. (2016). Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs. Pattern Recognition. 63. 476–486. 138 indexed citations
15.
Zhang, Yu, et al.. (2014). ECG-based frame selection and curvature-based ROI detection for measuring carotid intima-media thickness. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9040. 904016–904016. 2 indexed citations
16.
Tajbakhsh, Nima, et al.. (2014). Automatic polyp detection from learned boundaries. 8198. 97–100. 18 indexed citations
17.
Tajbakhsh, Nima, et al.. (2013). Motion Analysis of Right Ventricular Dysfunction Under Mild or Moderate Pressure Overload Caused by Acute Pulmonary Embolism. Ultrasound in Medicine & Biology. 39(11). 2066–2074.
18.
Tajbakhsh, Nima, Babak Nadjar Araabi, & Hamid Soltanian‐Zadeh. (2010). Robust Iris Verification Based on Local and Global Variations. EURASIP Journal on Advances in Signal Processing. 2010(1). 3 indexed citations
19.
Tajbakhsh, Nima, Babak Nadjar Araabi, & Hamid Soltanian‐Zadeh. (2008). Feature fusion as a practical solution toward noncooperative iris recognition. International Conference on Information Fusion. 1–7. 5 indexed citations
20.
Tajbakhsh, Nima, Babak Nadjar Araabi, & Hamid Soltanian‐Zadeh. (2008). An intelligent decision combiner applied to noncooperative iris recognition. International Conference on Information Fusion. 1–6. 4 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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