Anant Madabhushi

37.1k total citations · 14 hit papers
544 papers, 23.3k citations indexed

About

Anant Madabhushi is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Anant Madabhushi has authored 544 papers receiving a total of 23.3k indexed citations (citations by other indexed papers that have themselves been cited), including 320 papers in Radiology, Nuclear Medicine and Imaging, 221 papers in Artificial Intelligence and 161 papers in Computer Vision and Pattern Recognition. Recurrent topics in Anant Madabhushi's work include Radiomics and Machine Learning in Medical Imaging (275 papers), AI in cancer detection (214 papers) and Medical Image Segmentation Techniques (96 papers). Anant Madabhushi is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (275 papers), AI in cancer detection (214 papers) and Medical Image Segmentation Techniques (96 papers). Anant Madabhushi collaborates with scholars based in United States, China and Colombia. Anant Madabhushi's co-authors include Michael D. Feldman, Andrew Janowczyk, George Lee, Vamsidhar Velcheti, Hannah Gilmore, Kaustav Bera, John Tomaszewski, Ajay Basavanhally, Prateek Prasanna and Pallavi Tiwari and has published in prestigious journals such as Circulation, Nature Communications and Journal of Clinical Oncology.

In The Last Decade

Anant Madabhushi

518 papers receiving 22.8k citations

Hit Papers

Applications of machine l... 2009 2026 2014 2020 2019 2009 2019 2016 2016 500 1000 1.5k
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. Applications of machine learning in drug discovery and development
    2019 1.7k citations George Lee, Anant Madabhushi et al. profile →
  2. Histopathological Image Analysis: A Review
    2009 1.4k citations Metin N. Gürcan, Anant Madabhushi et al. profile →
  3. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
    2019 935 citations Kaustav Bera, Kurt A. Schalper et al. Nature Reviews Clinical Oncology profile →
  4. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
    2016 846 citations Andrew Janowczyk, Anant Madabhushi Journal of Pathology Informatics profile →
  5. Image analysis and machine learning in digital pathology: Challenges and opportunities
    2016 659 citations Anant Madabhushi, George Lee Medical Image Analysis profile →
  6. Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images
    2015 625 citations Hannah Gilmore, Anant Madabhushi et al. profile →
  7. Predicting cancer outcomes with radiomics and artificial intelligence in radiology
    2021 482 citations Kaustav Bera, Nathaniel Braman et al. Nature Reviews Clinical Oncology profile →
  8. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI
    2017 479 citations Nathaniel Braman, Maryam Etesami et al. profile →
  9. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks
    2014 377 citations Ajay Basavanhally, Hannah Gilmore et al. profile →
  10. Radiomics and radiogenomics in lung cancer: A review for the clinician
    2017 349 citations Rajat Thawani, Prateek Prasanna et al. profile →
  11. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent
    2017 349 citations Hannah Gilmore, Ajay Basavanhally et al. profile →
  12. A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images
    2016 337 citations Hannah Gilmore, Anant Madabhushi et al. profile →
  13. Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy forHER2 (ERBB2)–Positive Breast Cancer
    2019 235 citations Nathaniel Braman, Prateek Prasanna et al. profile →
  14. Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment
    2024 95 citations Anant Madabhushi et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anant Madabhushi United States 75 12.0k 11.2k 6.1k 4.1k 3.0k 544 23.3k
Jeroen van der Laak Netherlands 48 7.2k 0.6× 7.8k 0.7× 4.1k 0.7× 1.9k 0.5× 2.0k 0.7× 207 17.0k
Geert Litjens Netherlands 37 8.4k 0.7× 7.9k 0.7× 4.4k 0.7× 3.1k 0.8× 1.4k 0.5× 101 16.1k
Daniel L. Rubin United States 63 8.2k 0.7× 5.9k 0.5× 2.2k 0.4× 2.5k 0.6× 1.3k 0.4× 366 15.9k
Maryellen L. Giger United States 73 12.5k 1.0× 9.3k 0.8× 3.3k 0.5× 5.2k 1.3× 1.8k 0.6× 477 18.6k
Bram van Ginneken Netherlands 80 21.2k 1.8× 9.2k 0.8× 10.4k 1.7× 8.4k 2.1× 1.5k 0.5× 432 34.4k
Hao Chen China 56 5.9k 0.5× 6.8k 0.6× 9.7k 1.6× 1.2k 0.3× 1.8k 0.6× 354 19.4k
Francesco Ciompi Netherlands 28 6.6k 0.6× 5.7k 0.5× 3.4k 0.6× 2.1k 0.5× 1.2k 0.4× 103 12.6k
Michael D. Feldman United States 63 3.2k 0.3× 3.6k 0.3× 1.9k 0.3× 2.5k 0.6× 5.1k 1.7× 276 15.3k
Ronald M. Summers United States 62 9.3k 0.8× 5.2k 0.5× 4.3k 0.7× 3.5k 0.9× 2.5k 0.8× 501 18.9k
Anil V. Parwani United States 56 2.9k 0.2× 4.2k 0.4× 913 0.1× 3.6k 0.9× 2.7k 0.9× 493 13.5k

Countries citing papers authored by Anant Madabhushi

Since Specialization
Citations

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

Fields of papers citing papers by Anant Madabhushi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anant Madabhushi

This figure shows the co-authorship network connecting the top 25 collaborators of Anant Madabhushi. A scholar is included among the top collaborators of Anant Madabhushi 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 Anant Madabhushi. Anant Madabhushi 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.
Xu, Hongming, Duan‐Bo Shi, Huamin Qin, et al.. (2025). When multiple instance learning meets foundation models: Advancing histological whole slide image analysis. Medical Image Analysis. 101. 103456–103456. 6 indexed citations
2.
Corredor, Germán, et al.. (2025). Artificial intelligence in digital pathology — time for a reality check. Nature Reviews Clinical Oncology. 22(4). 283–291. 8 indexed citations
3.
Cheng, Yuan, Xiuming Zhang, Lei Hu, et al.. (2024). BEEx Is an Open-Source Tool That Evaluates Batch Effects in Medical Images to Enable Multicenter Studies. Cancer Research. 85(2). 218–230. 1 indexed citations
4.
Khorrami, Mohammadhadi, Nathaniel Braman, Siddharth Kunte, et al.. (2023). Radiomic predicts early response to CDK4/6 inhibitors in hormone receptor positive metastatic breast cancer. npj Breast Cancer. 9(1). 67–67. 4 indexed citations
5.
Koyuncu, Can, Cheng Lu, Rainer Grobholz, et al.. (2022). Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer. Medical Image Analysis. 84. 102702–102702. 15 indexed citations
6.
Braman, Nathaniel, Prateek Prasanna, Kaustav Bera, et al.. (2022). Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers. Clinical Cancer Research. 28(20). 4410–4424. 19 indexed citations
7.
Kar, Sudeshna Sil, Leina Lunasco, Xiangyi Meng, et al.. (2022). OCT-Derived Radiomic Features Predict Anti–VEGF Response and Durability in Neovascular Age-Related Macular Degeneration. SHILAP Revista de lepidopterología. 2(4). 100171–100171. 10 indexed citations
8.
Kar, Sudeshna Sil, et al.. (2021). Multi-Compartment OCT-derived Radiomics Features to predict Anti-VEGF Treatment Durability for Diabetic Macular Edema. Investigative Ophthalmology & Visual Science. 62(11). 3–3.
9.
Khorrami, Mohammadhadi, Kaustav Bera, Rajat Thawani, et al.. (2021). Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans. European Journal of Cancer. 148. 146–158. 24 indexed citations
10.
Bhargava, Hersh K., Patrick Leo, Robin Elliott, et al.. (2020). Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients. Clinical Cancer Research. 26(8). 1915–1923. 42 indexed citations
11.
Khorrami, Mohammadhadi, Prateek Prasanna, Amit Gupta, et al.. (2019). Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non–Small Cell Lung Cancer. Cancer Immunology Research. 8(1). 108–119. 200 indexed citations
12.
Bui, Marilyn M., Michael Riben, Kimberly H. Allison, et al.. (2018). Quantitative Image Analysis of Human Epidermal Growth Factor Receptor 2 Immunohistochemistry for Breast Cancer: Guideline From the College of American Pathologists. Archives of Pathology & Laboratory Medicine. 143(10). 1180–1195. 59 indexed citations
13.
Corredor, Germán, Xiangxue Wang, Yu Zhou, et al.. (2018). Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer. Clinical Cancer Research. 25(5). 1526–1534. 162 indexed citations
14.
Dennis, Adrienne T., et al.. (2017). Single cell qPCR reveals that additional HAND2 and microRNA-1 facilitate the early reprogramming progress of seven-factor-induced human myocytes. PLoS ONE. 12(8). e0183000–e0183000. 14 indexed citations
15.
Martel, Anne L., Çağlar Şenaras, Yu Zhou, et al.. (2017). An Image Analysis Resource for Cancer Research: PIIP—Pathology Image Informatics Platform for Visualization, Analysis, and Management. Cancer Research. 77(21). e83–e86. 42 indexed citations
16.
Lee, George, Robert W. Veltri, Guangjing Zhu, et al.. (2016). Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings. European Urology Focus. 3(4-5). 457–466. 38 indexed citations
17.
Varadan, Vinay, Sitharthan Kamalakaran, Hannah Gilmore, et al.. (2015). Brief‐exposure to preoperative bevacizumab reveals a TGF‐β signature predictive of response in HER2‐negative breast cancers. International Journal of Cancer. 138(3). 747–757. 14 indexed citations
18.
Basavanhally, Ajay, Michael D. Feldman, Natalie Shih, et al.. (2012). Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX. Journal of Pathology Informatics. 2(2). 1–1. 45 indexed citations
19.
Madabhushi, Anant, Jason Dowling, Henkjan Huisman, & Dean C. Barratt. (2011). Prostate Cancer Imaging. Image Analysis and Image-Guided Interventions: International Workshop, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 2011, Proceedings. Springer eBooks. 2 indexed citations
20.
Tiwari, Pallavi, Mark Rosen, & Anant Madabhushi. (2008). Consensus-Locally Linear Embedding (C-LLE): Application to Prostate Cancer Detection on Magnetic Resonance Spectroscopy. Lecture notes in computer science. 11(Pt 2). 330–338. 14 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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