Ashirbani Saha

3.6k total citations · 1 hit paper
69 papers, 2.3k citations indexed

About

Ashirbani Saha is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ashirbani Saha has authored 69 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Radiology, Nuclear Medicine and Imaging, 26 papers in Artificial Intelligence and 17 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ashirbani Saha's work include Radiomics and Machine Learning in Medical Imaging (32 papers), AI in cancer detection (24 papers) and MRI in cancer diagnosis (13 papers). Ashirbani Saha is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (32 papers), AI in cancer detection (24 papers) and MRI in cancer diagnosis (13 papers). Ashirbani Saha collaborates with scholars based in Canada, United States and United Kingdom. Ashirbani Saha's co-authors include Maciej A. Mazurowski, Mateusz Buda, Michael R. Harowicz, Mustafa R. Bashir, Q. M. Jonathan Wu, Ehab A. AlBadawy, Zhe Zhu, Lars J. Grimm, Sujata V. Ghate and Ruth Walsh and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Image Processing and Journal of neurosurgery.

In The Last Decade

Ashirbani Saha

65 papers receiving 2.2k citations

Hit Papers

Deep learning in radiolog... 2018 2026 2020 2023 2018 100 200 300
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. 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 Career Trend Papers Cites
Ashirbani Saha Canada 24 1.3k 890 506 289 230 69 2.3k
Jens Kleesiek Germany 21 994 0.8× 503 0.6× 501 1.0× 436 1.5× 385 1.7× 107 2.3k
Ahmad Chaddad Canada 27 1.4k 1.0× 715 0.8× 317 0.6× 342 1.2× 443 1.9× 111 2.4k
Mitko Veta Netherlands 20 992 0.8× 1.3k 1.4× 767 1.5× 123 0.4× 199 0.9× 59 2.0k
Tao Tan China 23 1.0k 0.8× 924 1.0× 459 0.9× 122 0.4× 319 1.4× 122 2.0k
Bruce A. Vendt United States 8 2.1k 1.6× 959 1.1× 739 1.5× 348 1.2× 802 3.5× 9 3.3k
Jun Xia China 25 2.0k 1.5× 1.2k 1.4× 378 0.7× 261 0.9× 464 2.0× 124 4.0k
Yuchen Qiu United States 18 1.1k 0.8× 859 1.0× 331 0.7× 161 0.6× 271 1.2× 62 1.8k
Ajay Basavanhally United States 19 1.1k 0.9× 1.8k 2.1× 1.0k 2.0× 135 0.5× 111 0.5× 28 2.3k
Narges Razavian United States 14 1.2k 0.9× 1.4k 1.6× 303 0.6× 124 0.4× 371 1.6× 39 2.7k
Ashish Sharma United States 25 711 0.5× 1.1k 1.2× 330 0.7× 88 0.3× 392 1.7× 81 2.6k

Countries citing papers authored by Ashirbani Saha

Since Specialization
Citations

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

Fields of papers citing papers by Ashirbani Saha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ashirbani Saha

This figure shows the co-authorship network connecting the top 25 collaborators of Ashirbani Saha. A scholar is included among the top collaborators of Ashirbani Saha 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 Ashirbani Saha. Ashirbani Saha 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.
Zhou, Fangwen, Muhammad Afzal, Ashirbani Saha, et al.. (2025). Benchmarking domain-specific pretrained language models to identify the best model for methodological rigor in clinical studies. Journal of Biomedical Informatics. 166. 104825–104825. 1 indexed citations
2.
Saha, Ashirbani, et al.. (2025). Development and evaluation of large-language models (LLMs) for oncology: A scoping review. PLOS Digital Health. 4(8). e0000980–e0000980.
3.
Saha, Ashirbani, et al.. (2023). Measurement of adverse cosmesis in breast cancer: A deep learning approach. Expert Systems with Applications. 238. 122209–122209.
4.
5.
Petch, Jeremy, Christopher Pettengell, Gregory R. Pond, et al.. (2023). Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center. JCO Clinical Cancer Informatics. 7(7). e2200182–e2200182. 5 indexed citations
6.
Saha, Ashirbani, et al.. (2023). A scoping review of natural language processing of radiology reports in breast cancer. Frontiers in Oncology. 13. 1160167–1160167. 11 indexed citations
7.
Chen, Xuhang, Chi‐Man Pun, Guoli Huang, et al.. (2023). Generative AI for brain image computing and brain network computing: a review. Frontiers in Neuroscience. 17. 1203104–1203104. 58 indexed citations
8.
Yao, Xiaomei, et al.. (2023). Evaluating the efficacy of artificial intelligence tools for the automation of systematic reviews in cancer research: A systematic review. Cancer Epidemiology. 88. 102511–102511. 14 indexed citations
9.
Cusimano, Michael D., et al.. (2021). Cognitive Dysfunction, Brain Volumes, and Traumatic Brain Injury in Homeless Persons. SHILAP Revista de lepidopterología. 2(1). 136–148. 6 indexed citations
10.
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
11.
Thomas, Samantha M., et al.. (2020). Performance of preoperative breast MRI based on breast cancer molecular subtype. Clinical Imaging. 67. 130–135. 4 indexed citations
12.
Zádor, Zsolt, Alexander Landry, Ashirbani Saha, & Michael D. Cusimano. (2020). Gene Expression Signatures Identify Biologically Homogenous Subgroups of Grade 2 Meningiomas. Frontiers in Oncology. 10. 541928–541928. 4 indexed citations
13.
14.
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 →
15.
Saha, Ashirbani, Michael R. Harowicz, Lars J. Grimm, et al.. (2018). A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. British Journal of Cancer. 119(4). 508–516. 187 indexed citations
16.
Saha, Ashirbani, Michael R. Harowicz, Allison Hall, et al.. (2018). Intra-tumor molecular heterogeneity in breast cancer: definitions of measures and association with distant recurrence-free survival. Breast Cancer Research and Treatment. 172(1). 123–132. 7 indexed citations
17.
Saha, Ashirbani, Michael R. Harowicz, & Maciej A. Mazurowski. (2018). Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors. Medical Physics. 45(7). 3076–3085. 49 indexed citations
18.
Grimm, Lars J., Ashirbani Saha, Sujata V. Ghate, et al.. (2018). Relationship between Background Parenchymal Enhancement on High-risk Screening MRI and Future Breast Cancer Risk. Academic Radiology. 26(1). 69–75. 39 indexed citations
19.
Paredes, David, Ashirbani Saha, & Maciej A. Mazurowski. (2017). Deep learning for segmentation of brain tumors: can we train with images from different institutions?. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10134. 101341P–101341P. 6 indexed citations
20.
Saha, Ashirbani, Lars J. Grimm, Michael R. Harowicz, et al.. (2016). Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics. Medical Physics. 43(8Part1). 4558–4564. 24 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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