Deepta Rajan

821 total citations · 1 hit paper
19 papers, 400 citations indexed

About

Deepta Rajan is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Deepta Rajan has authored 19 papers receiving a total of 400 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Deepta Rajan's work include Machine Learning in Healthcare (5 papers), COVID-19 diagnosis using AI (5 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Deepta Rajan is often cited by papers focused on Machine Learning in Healthcare (5 papers), COVID-19 diagnosis using AI (5 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Deepta Rajan collaborates with scholars based in United States, Switzerland and India. Deepta Rajan's co-authors include Jayaraman J. Thiagarajan, Andreas Spanias, Huan Song, David Beymer, Pavan Turaga, Mahesh K. Banavar, Suhas Ranganath, Emmanouíl Karteris, Jan Lukas Robertus and Karthikeyan Natesan Ramamurthy and has published in prestigious journals such as Scientific Reports, Patterns and International Journal of Artificial Intelligence Tools.

In The Last Decade

Deepta Rajan

18 papers receiving 386 citations

Hit Papers

align trajectories

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.

Attend and Diagnose: Clinical Time Series Analysis Using Attention Models

2018 264 citations Huan Song, Deepta Rajan et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Deepta Rajan United States	8	213	96	64	59	43	19	400
Karl Øyvind Mikalsen Norway	11	285 1.3×	99 1.0×	39 0.6×	52 0.9×	19 0.4×	21	441
Ketan Gupta United States	12	99 0.5×	26 0.3×	88 1.4×	43 0.7×	16 0.4×	44	417
Vitaly Schetinin United Kingdom	14	235 1.1×	53 0.6×	22 0.3×	38 0.6×	12 0.3×	40	461
Teresa Rocha Portugal	11	113 0.5×	50 0.5×	75 1.2×	32 0.5×	13 0.3×	52	348
S. Paredes Portugal	10	113 0.5×	49 0.5×	77 1.2×	29 0.5×	13 0.3×	48	314
Huan Song United States	8	261 1.2×	93 1.0×	60 0.9×	12 0.2×	45 1.0×	22	447
Moxian Song China	13	199 0.9×	46 0.5×	10 0.2×	32 0.5×	52 1.2×	21	379
Nasmin Jiwani United States	14	100 0.5×	27 0.3×	90 1.4×	46 0.8×	15 0.3×	37	446
Jiao Yin China	11	131 0.6×	60 0.6×	12 0.2×	19 0.3×	64 1.5×	35	493
Ahmed Alahmadi Saudi Arabia	11	159 0.7×	28 0.3×	102 1.6×	41 0.7×	12 0.3×	39	477

Countries citing papers authored by Deepta Rajan

Since Specialization

Citations

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

Fields of papers citing papers by Deepta Rajan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Deepta Rajan

This figure shows the co-authorship network connecting the top 25 collaborators of Deepta Rajan. A scholar is included among the top collaborators of Deepta Rajan 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 Deepta Rajan. Deepta Rajan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

19 of 19 papers shown

Raman, Ramakrishnan, et al.. (2025). Sentiment-Driven Knowledge Graph Attention Network for Enhanced Recommendation Systems. 1–6.

Anirudh, Rushil, et al.. (2023). Exploring Inlier and Outlier Specification for Improved Medical OOD Detection. 4591–4600. 1 indexed citations

Thiagarajan, Jayaraman J., et al.. (2022). Training calibration-based counterfactual explainers for deep learning models in medical image analysis. Scientific Reports. 12(1). 597–597. 20 indexed citations

Born, Jannis, David Beymer, Deepta Rajan, et al.. (2021). On the role of artificial intelligence in medical imaging of COVID-19. Patterns. 2(6). 100269–100269. 38 indexed citations

Born, Jannis, David Beymer, Deepta Rajan, et al.. (2021). On the role of artificial intelligence in medical imaging of COVID-19. Patterns. 2(8). 100330–100330. 11 indexed citations

Lee, Joshua, Deepta Rajan, Prasanna Sattigeri, et al.. (2021). Fair Selective Classification Via Sufficiency. 6076–6086. 3 indexed citations

Rajan, Deepta, Jayaraman J. Thiagarajan, Alexandros Karargyris, & Satyananda Kashyap. (2020). Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification.. arXiv (Cornell University). 2 indexed citations

Shi, Luyao, et al.. (2020). Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study. 743–754. 2 indexed citations

Thiagarajan, Jayaraman J., et al.. (2020). DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms. Scientific Reports. 10(1). 16428–16428. 11 indexed citations

10.

Spanias, Andreas, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, et al.. (2020). E-Book on DSP Theory with Interactive iOS, Java, and Android Simulations. 23.445.1–23.445.14. 2 indexed citations

11.

Ranganath, Suhas, Jayaraman J. Thiagarajan, Deepta Rajan, et al.. (2019). Interactive Signal Processing Education Applications for the Android Platform. 10(2). 1 indexed citations

12.

Thiagarajan, Jayaraman J., Deepta Rajan, & Prasanna Sattigeri. (2018). Can Deep Clinical Models Handle Real-World Domain Shifts?. arXiv (Cornell University). 1 indexed citations

13.

Song, Huan, Deepta Rajan, Jayaraman J. Thiagarajan, & Andreas Spanias. (2018). Attend and Diagnose: Clinical Time Series Analysis Using Attention Models. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 264 indexed citations breakdown →

14.

Rajan, Deepta, David Beymer, & Girish Narayan. (2018). Improving Generalization of Deep Models for Cardiac Disease Detection Using Limited Channel ECG. Computing in cardiology. 8 indexed citations

15.

Banavar, Mahesh K., et al.. (2014). Embedding Android signal processing apps in a high school math class — An RET project. 11. 1–4. 6 indexed citations

16.

Thiagarajan, Jayaraman J., et al.. (2014). Kernel Sparse Models for Automated Tumor Segmentation. International Journal of Artificial Intelligence Tools. 23(3). 1460004–1460004. 11 indexed citations

17.

Rajan, Deepta, et al.. (2013). Development of mobile sensing apps for DSP applications. 778. 323–328. 3 indexed citations

18.

Rajan, Deepta, Andreas Spanias, Suhas Ranganath, Mahesh K. Banavar, & Photini Spanias. (2013). Health monitoring laboratories by interfacing physiological sensors to mobile android devices. 52. 1049–1055. 13 indexed citations

19.

Thiagarajan, Jayaraman J., Deepta Rajan, Karthikeyan Natesan Ramamurthy, David Frakes, & Andreas Spanias. (2012). Automated tumor segmentation using kernel sparse representations. 2. 401–406. 3 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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