Saptarshi Chatterjee

682 total citations
36 papers, 464 citations indexed

About

Saptarshi Chatterjee is a scholar working on Oncology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Saptarshi Chatterjee has authored 36 papers receiving a total of 464 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Oncology, 11 papers in Artificial Intelligence and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Saptarshi Chatterjee's work include Cutaneous Melanoma Detection and Management (9 papers), AI in cancer detection (8 papers) and Statistical Methods in Clinical Trials (5 papers). Saptarshi Chatterjee is often cited by papers focused on Cutaneous Melanoma Detection and Management (9 papers), AI in cancer detection (8 papers) and Statistical Methods in Clinical Trials (5 papers). Saptarshi Chatterjee collaborates with scholars based in India, United States and Japan. Saptarshi Chatterjee's co-authors include Debangshu Dey, Sugata Munshi, Kallol Purkait, Arindam Mukherjee, Xin Huang, Yan Sun, Lü Tian, Viswanath Devanarayan, Paul Trow and Shrabanti Chowdhury and has published in prestigious journals such as Expert Systems with Applications, Statistics in Medicine and Dalton Transactions.

In The Last Decade

Saptarshi Chatterjee

32 papers receiving 441 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Saptarshi Chatterjee India 13 185 147 145 74 67 36 464
Rong Ma United States 12 81 0.4× 80 0.5× 15 0.1× 66 0.9× 39 0.6× 34 499
Ladislav Rampášek Canada 6 35 0.2× 71 0.5× 50 0.3× 22 0.3× 26 0.4× 8 532
Thomas Schräder Germany 11 55 0.3× 195 1.3× 77 0.5× 36 0.5× 21 0.3× 44 395
Sarah Zhang United States 10 34 0.2× 122 0.8× 49 0.3× 52 0.7× 37 0.6× 21 526
M. Rajasekhar Reddy India 8 57 0.3× 44 0.3× 56 0.4× 27 0.4× 31 0.5× 16 386
Jinlu Liu China 14 74 0.4× 86 0.6× 19 0.1× 36 0.5× 108 1.6× 32 552
Po‐Ting Chen Taiwan 11 198 1.1× 152 1.0× 211 1.5× 14 0.2× 55 0.8× 39 467
Qing Xia China 11 61 0.3× 56 0.4× 78 0.5× 111 1.5× 36 0.5× 61 487
Jili Chen China 11 28 0.2× 43 0.3× 163 1.1× 20 0.3× 18 0.3× 43 412

Countries citing papers authored by Saptarshi Chatterjee

Since Specialization
Citations

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

Fields of papers citing papers by Saptarshi Chatterjee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saptarshi Chatterjee

This figure shows the co-authorship network connecting the top 25 collaborators of Saptarshi Chatterjee. A scholar is included among the top collaborators of Saptarshi Chatterjee 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 Saptarshi Chatterjee. Saptarshi Chatterjee 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.
2.
Chatterjee, Saptarshi, et al.. (2023). PY-Net: Rethinking segmentation frameworks with dense pyramidal operations for optic disc and cup segmentation from retinal fundus images. Biomedical Signal Processing and Control. 85. 104895–104895. 12 indexed citations
3.
Chatterjee, Saptarshi, et al.. (2022). Cross Empirical Wavelet Transform Aided Feature Learning Network for Detection of Localized Faults in Deep Groove Bearings. Electric Power Components and Systems. 50(4-5). 269–281.
4.
Chatterjee, Saptarshi, et al.. (2021). Suicidal Deaths Amid COVID-19 Pandemic: A Cross-Sectional Autopsy-Based Study. Indian Journal of Forensic Medicine & Toxicology. 15(4). 2696–2704. 2 indexed citations
5.
Chatterjee, Saptarshi, et al.. (2021). Adaptive morphology aided 2-pathway convolutional neural network for lung nodule classification. Biomedical Signal Processing and Control. 72. 103347–103347. 37 indexed citations
6.
Chatterjee, Saptarshi, et al.. (2020). Dermatological expert system implementing the ABCD rule of dermoscopy for skin disease identification. Expert Systems with Applications. 167. 114204–114204. 21 indexed citations
7.
Chatterjee, Saptarshi, et al.. (2020). An adaptive morphology based segmentation technique for lung nodule detection in thoracic CT image. Computer Methods and Programs in Biomedicine. 197. 105720–105720. 30 indexed citations
8.
Chatterjee, Saptarshi, et al.. (2020). Superpixel and Density Based Region Segmentation Algorithm for Lung Nodule Detection. 511–515. 5 indexed citations
9.
Chatterjee, Saptarshi, Debangshu Dey, & Sugata Munshi. (2019). Integration of morphological preprocessing and fractal based feature extraction with recursive feature elimination for skin lesion types classification. Computer Methods and Programs in Biomedicine. 178. 201–218. 91 indexed citations
10.
Chatterjee, Saptarshi, et al.. (2019). A trans-dichloridoplatinum(II) complex of a monodentate nitrogen mustard: Synthesis, stability and cytotoxicity studies. Journal of Inorganic Biochemistry. 204. 110982–110982. 3 indexed citations
11.
Chatterjee, Saptarshi, et al.. (2019). Extraction of features from cross correlation in space and frequency domains for classification of skin lesions. Biomedical Signal Processing and Control. 53. 101581–101581. 42 indexed citations
12.
Chatterjee, Saptarshi, et al.. (2018). Group regularization for zero‐inflated negative binomial regression models with an application to health care demand in Germany. Statistics in Medicine. 37(20). 3012–3026. 8 indexed citations
13.
Chowdhury, Shrabanti, et al.. (2018). Group regularization for zero-inflated poisson regression models with an application to insurance ratemaking. Journal of Applied Statistics. 46(9). 1567–1581. 8 indexed citations
14.
Mallick, Himel, et al.. (2018). A Note on the Adaptive LASSO for Zero-Inflated Poisson Regression. Journal of Probability and Statistics. 2018. 1–9. 7 indexed citations
15.
Huang, Xin, Yan Sun, Paul Trow, et al.. (2017). Patient subgroup identification for clinical drug development. Statistics in Medicine. 36(9). 1414–1428. 34 indexed citations
16.
Chatterjee, Saptarshi, et al.. (2016). Anticancer activity of a chelating nitrogen mustard bearing tetrachloridoplatinum(iv) complex: better stability yet equipotent to the Pt(ii) analogue. Dalton Transactions. 45(29). 11710–11722. 23 indexed citations
17.
Chatterjee, Saptarshi, Debangshu Dey, & Sugata Munshi. (2015). Mathematical morphology aided shape, texture and colour feature extraction from skin lesion for identification of malignant melanoma. 200–203. 12 indexed citations
18.
Nath, Asoke, et al.. (2014). Bit Level Multi Way Feedback Encryption Standard version-2(BLMWFES-2). 3. 1702–1707. 1 indexed citations
19.
Chatterjee, Saptarshi, et al.. (2013). MODIFIED MULTI WAY FEEDBACK ENCRYPTION STANDARDVER-2(MWFES-2). Journal of Global Research in Computer Sciences. 4(12). 8–13. 5 indexed citations
20.
Islam, Sk. Manirul, Anupam Singha Roy, Sasanka Dalapati, et al.. (2013). Synthesis, crystal structure and spectroscopic studies of a cobalt(III) Schiff base complex and its use as a heterogeneous catalyst for the oxidation reaction under mild condition. Journal of Molecular Catalysis A Chemical. 380. 94–103. 22 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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