Abhir Bhalerao

2.4k total citations
77 papers, 994 citations indexed

About

Abhir Bhalerao is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Abhir Bhalerao has authored 77 papers receiving a total of 994 indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Computer Vision and Pattern Recognition, 22 papers in Radiology, Nuclear Medicine and Imaging and 17 papers in Artificial Intelligence. Recurrent topics in Abhir Bhalerao's work include Medical Image Segmentation Techniques (27 papers), Image Retrieval and Classification Techniques (13 papers) and Retinal Imaging and Analysis (8 papers). Abhir Bhalerao is often cited by papers focused on Medical Image Segmentation Techniques (27 papers), Image Retrieval and Classification Techniques (13 papers) and Retinal Imaging and Analysis (8 papers). Abhir Bhalerao collaborates with scholars based in United Kingdom, United States and Egypt. Abhir Bhalerao's co-authors include Constantino Carlos Reyes‐Aldasoro, C.-F. Westin, Shinichi Tamura, S. Nakajima, Nobuyuki Shiraga, Yoshinobu Sato, R. Kikinis, Roland Wilson, Li Wang and R. Wilson and has published in prestigious journals such as Journal of Clinical Oncology, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing.

In The Last Decade

Abhir Bhalerao

73 papers receiving 947 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Abhir Bhalerao United Kingdom 15 467 303 178 107 102 77 994
Konstantinos K. Delibasis Greece 17 445 1.0× 343 1.1× 135 0.8× 162 1.5× 99 1.0× 91 984
Xiaoguang Li China 16 404 0.9× 115 0.4× 146 0.8× 37 0.3× 56 0.5× 120 899
Huisi Wu China 20 860 1.8× 497 1.6× 557 3.1× 112 1.0× 142 1.4× 76 1.6k
Jianan Li China 17 594 1.3× 226 0.7× 152 0.9× 96 0.9× 101 1.0× 103 1.1k
Christian Roux France 21 903 1.9× 720 2.4× 195 1.1× 279 2.6× 191 1.9× 118 1.7k
Miao Liao China 25 908 1.9× 244 0.8× 184 1.0× 31 0.3× 134 1.3× 102 1.6k
Gady Agam United States 15 544 1.2× 222 0.7× 201 1.1× 41 0.4× 36 0.4× 90 999
James Ford United States 24 313 0.7× 260 0.9× 477 2.7× 26 0.2× 95 0.9× 74 2.0k
Benoît Macq Belgium 21 1.5k 3.2× 194 0.6× 180 1.0× 9 0.1× 106 1.0× 111 2.2k

Countries citing papers authored by Abhir Bhalerao

Since Specialization
Citations

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

Fields of papers citing papers by Abhir Bhalerao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Abhir Bhalerao

This figure shows the co-authorship network connecting the top 25 collaborators of Abhir Bhalerao. A scholar is included among the top collaborators of Abhir Bhalerao 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 Abhir Bhalerao. Abhir Bhalerao 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.
Wang, Jun, Lixing Zhu, Xiaohan Yu, Abhir Bhalerao, & Yulan He. (2025). Improving Medical Visual Representation Learning with Pathological-level Cross-Modal Alignment and Correlation Exploration. IEEE Journal of Biomedical and Health Informatics. PP. 1–12.
2.
Wei, Wenbo, Jun Wang, & Abhir Bhalerao. (2025). COCO-OLAC: A Benchmark for Occluded Panoptic Segmentation and Image Understanding. 1–5.
3.
Lashen, Ayat, Noorul Wahab, Michael S. Toss, et al.. (2024). Characterization of Breast Cancer Intra-Tumor Heterogeneity Using Artificial Intelligence. Cancers. 16(22). 3849–3849. 1 indexed citations
4.
Wang, Jun, et al.. (2024). CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation. IEEE Journal of Biomedical and Health Informatics. 28(4). 2199–2210. 18 indexed citations
5.
6.
Lu, Wenqi, Ayat Lashen, Noorul Wahab, et al.. (2023). AI‐based intra‐tumor heterogeneity score of Ki67 expression as a prognostic marker for early‐stage ER+/HER2− breast cancer. The Journal of Pathology Clinical Research. 10(1). e346–e346. 4 indexed citations
7.
Wahab, Noorul, Michael S. Toss, Asmaa Ibrahim, et al.. (2023). Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence. British Journal of Cancer. 129(11). 1747–1758. 22 indexed citations
8.
Williams, Mark A., et al.. (2018). Markov random field segmentation for industrial computed tomography with metal artefacts. Journal of X-Ray Science and Technology. 26(4). 573–591. 5 indexed citations
9.
Bhalerao, Abhir, et al.. (2018). Dual Viewpoint Passenger State Classification Using 3D CNNs. Warwick Research Archive Portal (University of Warwick). 2163–2169. 4 indexed citations
10.
Zhang, Qiang, Abhir Bhalerao, & C. Richard Hutchinson. (2017). Deformable appearance pyramids for anatomy representation, landmark detection and pathology classification. International Journal of Computer Assisted Radiology and Surgery. 12(8). 1271–1280. 3 indexed citations
11.
Zhang, Qiang, Abhir Bhalerao, Emma Helm, & Charles Hutchinson. (2015). Active shape model unleashed with multi-scale local appearance. Warwick Research Archive Portal (University of Warwick). 4664–4668. 3 indexed citations
12.
Bhalerao, Abhir, Luke Pase, Graham J. Lieschke, Stephen A. Renshaw, & Constantino Carlos Reyes‐Aldasoro. (2012). Local affine texture tracking for serial registration of zebrafish images. City Research Online (City University London). 434–437.
13.
Martin, Graham R., et al.. (2010). Local Affine Image Matching and Synthesis Based on Structural Patterns. IEEE Transactions on Image Processing. 19(8). 1968–1977. 2 indexed citations
14.
Bhalerao, Abhir & Nasir Rajpoot. (2010). Proceedings of medical image understanding and analysis 2010. 15 indexed citations
15.
Bhalerao, Abhir & Carl‐Fredrik Westin. (2007). Hyperspherical von Mises-Fisher Mixture (HvMF) Modelling of High Angular Resolution Diffusion MRI. Lecture notes in computer science. 236–243. 13 indexed citations
16.
Wang, Li, Abhir Bhalerao, & Roland Wilson. (2007). Analysis of Retinal Vasculature Using a Multiresolution Hermite Model. IEEE Transactions on Medical Imaging. 26(2). 137–152. 75 indexed citations
17.
Reyes‐Aldasoro, Constantino Carlos & Abhir Bhalerao. (2003). Volumetric Texture Description and Discriminant Feature Selection for MRI. Lecture notes in computer science. 18. 282–293. 18 indexed citations
18.
Westin, C.-F., Abhir Bhalerao, Hans Knutsson, & Ron Kikinis. (2002). Using local 3D structure for segmentation of bone from computer tomography images. 794–800. 23 indexed citations
19.
Bhalerao, Abhir & R. Wilson. (2001). Estimating Local and Global Image Structure using a Gaussian Intensity Model. 1 indexed citations
20.
Bhalerao, Abhir & R. Wilson. (2001). Unsupervised image segmentation combining region and boundary estimation. Image and Vision Computing. 19(6). 353–368. 12 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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