Ajay Basavanhally

3.2k citations

28 papers · 2.3k · 2 hit papers · h-index 19

Impact in

Biophysics top 0.5%
- Cell Image Analysis Techniques
Artificial Intelligence top 0.5%
- AI in cancer detection

Papers in

Artificial Intelligence 25
- AI in cancer detection 25
Computer Vision and Pattern Recognition 16
- Digital Imaging for Blood Diseases 7
- Medical Image Segmentation Techniques 7
- Image Retrieval and Classification Techniques 5

Co-authors: Anant Madabhushi (26 shared papers)Shridar Ganesan (14 shared papers)Michael D. Feldman (13 shared papers)John Tomaszewski (12 shared papers)Natalie Shih (8 shared papers)Fabio A. González (6 shared papers)Ángel Cruz-Roa (6 shared papers)Hannah Gilmore (6 shared papers)
Journals: IEEE Transactions on Biomedical Engineering (3 papers)Computerized Medical Imaging and Graphics (2 papers)PLoS ONE (2 papers)Scientific Reports (1 paper)Clinical Chemistry and Laboratory Medicine (CCLM) (1 paper)
Partner nations: United States Colombia

In The Last Decade

Ajay Basavanhally

28 papers receiving 2.2k citations

Hit Papers

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Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent 2017 · 349 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2017 Scientific Reports
2014 Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

Peers

Countries citing papers authored by Ajay Basavanhally

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Citations

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

Fields of papers citing papers by Ajay Basavanhally

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ajay Basavanhally, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ajay Basavanhally Line = papers co-authored together Ajay Basavanhally links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·Ángel Cruz-Roa, Ajay Basavanhally, Fabio A. González, Hannah Gilmore, Michael D. Feldman, Shridar Ganesan, Natalie Shih, John Tomaszewski, Anant Madabhushi Hit paper breakdown →	2014	377
2	Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent Scientific Reports ·Ángel Cruz-Roa, Hannah Gilmore, Ajay Basavanhally, Michael D. Feldman, Shridar Ganesan, Natalie Shih, John Tomaszewski, Fabio A. González, Anant Madabhushi Hit paper breakdown →	2017	349
3	Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features Journal of Medical Imaging ·Haibo Wang, Ángel Cruz-Roa, Ajay Basavanhally, Hannah Gilmore, Natalie Shih, Mike Feldman, John Tomaszewski, Fabio A. González, Anant Madabhushi	2014	249
4	Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+ Breast Cancer Histopathology IEEE Transactions on Biomedical Engineering ·Ajay Basavanhally, Shridar Ganesan, Shannon C. Agner, James Monaco, Michael D. Feldman, J E Tomaszewski, Gyan Bhanot, Anant Madabhushi	2009	207
5	Expectation–Maximization-Driven Geodesic Active Contour With Overlap Resolution (EMaGACOR): Application to Lymphocyte Segmentation on Breast Cancer Histopathology IEEE Transactions on Biomedical Engineering ·Hussain Fatakdawala, Jun Xu, Ajay Basavanhally, Gyan Bhanot, Shridar Ganesan, Michael D. Feldman, John E. Tomaszewski, Anant Madabhushi	2010	160
6	Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology Computerized Medical Imaging and Graphics ·Andrew Janowczyk, Ajay Basavanhally, Anant Madabhushi	2016	151
7	Multi-Field-of-View Framework for Distinguishing Tumor Grade in ER+ Breast Cancer From Entire Histopathology Slides IEEE Transactions on Biomedical Engineering ·Ajay Basavanhally, Shridar Ganesan, Michael D. Feldman, Natalie Shih, Carolyn Mies, John Tomaszewski, Anant Madabhushi	2013	101
8	High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection PLoS ONE ·Ángel Cruz-Roa, Hannah Gilmore, Ajay Basavanhally, Michael D. Feldman, Shridar Ganesan, Natalie Shih, John Tomaszewski, Anant Madabhushi, Fabio A. González	2018	95
9	Computer-aided prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data Computerized Medical Imaging and Graphics ·Anant Madabhushi, Shannon C. Agner, Ajay Basavanhally, Scott Doyle, George Lee	2011	94
10	Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·Haibo Wang, Ángel Cruz-Roa, Ajay Basavanhally, Hannah Gilmore, Natalie Shih, Mike Feldman, John Tomaszewski, Fabio A. González, Anant Madabhushi	2014	83
11	In vivo gated 4D imaging of the embryonic heart using optical coherence tomography Journal of Biomedical Optics ·Michael W. Jenkins, Osman Q. Chughtai, Ajay Basavanhally, Michiko Watanabe, Andrew M. Rollins	2007	76
12	Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·Ajay Basavanhally, Elaine Yu, Jun Xu, Shridar Ganesan, Michael D. Feldman, John Tomaszewski, Anant Madabhushi	2011	52
13	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 ·Ajay Basavanhally, Michael D. Feldman, Natalie Shih, Carolyn Mies, John Tomaszewski, Shridar Ganesan, Anant Madabhushi	2012	45
14	EM-based segmentation-driven color standardization of digitized histopathology Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·Ajay Basavanhally, Anant Madabhushi	2013	40
15	Expectation Maximization driven Geodesic Active Contour with Overlap Resolution (EMaGACOR): Application to Lymphocyte Segmentation on Breast Cancer Histopathology View ·Hussain Fatakdawala, Ajay Basavanhally, Jun Xu, Gyan Bhanot, Shridar Ganesan, Michael D. Feldman, John Tomaszewski, Anant Madabhushi	2009	39
16	Towards Improved Cancer Diagnosis and Prognosis Using Analysis of Gene Expression Data and Computer Aided Imaging Experimental Biology and Medicine ·Gabriela Alexe, James Monaco, Scott Doyle, Ajay Basavanhally, Anupama Reddy, Michael Seiler, Shridar Ganesan, Gyan Bhanot, Anant Madabhushi	2009	32
17	Integrated diagnostics: a conceptual framework with examples Clinical Chemistry and Laboratory Medicine (CCLM) ·Anant Madabhushi, Scott Doyle, George Lee, Ajay Basavanhally, James Monaco, Steve Masters, John Tomaszewski, Michael D. Feldman	2010	29
18	Computer-aided prognosis of ER+ breast cancer histopathology and correlating survival outcome with Oncotype DX assay Ajay Basavanhally, Jun Xu, Anant Madabhushi, Shridar Ganesan	2009	25
19	A boosted distance metric: application to content based image retrieval and classification of digitized histopathology Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·Jay Naik, Scott Doyle, Ajay Basavanhally, Shridar Ganesan, Michael D. Feldman, John E. Tomaszewski, Anant Madabhushi	2009	24
20	Predicting classifier performance with a small training set: Applications to computer-aided diagnosis and prognosis Ajay Basavanhally, Scott Doyle, Anant Madabhushi	2010	12

About Ajay Basavanhally

Ajay Basavanhally is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Molecular Biology and Biophysics, having authored 28 papers that have together received 2.3k indexed citations. Recurring topics across this work include AI in cancer detection (25 papers), Radiomics and Machine Learning in Medical Imaging (13 papers), Digital Imaging for Blood Diseases (7 papers), Medical Image Segmentation Techniques (7 papers), Gene expression and cancer classification (7 papers), Cell Image Analysis Techniques (5 papers), Image Retrieval and Classification Techniques (5 papers) and Breast Cancer Treatment Studies (3 papers). The work is most often cited by research in Biophysics (395 citations), Artificial Intelligence (1.8k citations), Health Informatics (75 citations), Computer Vision and Pattern Recognition (1.0k citations) and Radiology, Nuclear Medicine and Imaging (1.1k citations). Ajay Basavanhally has collaborated with scholars based in United States and Colombia. Frequent co-authors include Anant Madabhushi, Shridar Ganesan, Michael D. Feldman, John Tomaszewski, Natalie Shih, Fabio A. González, Ángel Cruz-Roa, Hannah Gilmore, Gyan Bhanot and Andrew Janowczyk. Their work appears in journals such as IEEE Transactions on Biomedical Engineering, Computerized Medical Imaging and Graphics, PLoS ONE, Scientific Reports and Clinical Chemistry and Laboratory Medicine (CCLM).

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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