Avishek Chatterjee

2.0k total citations · 1 hit paper
38 papers, 1.2k citations indexed

About

Avishek Chatterjee is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Avishek Chatterjee has authored 38 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Radiology, Nuclear Medicine and Imaging, 12 papers in Artificial Intelligence and 9 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Avishek Chatterjee's work include Radiomics and Machine Learning in Medical Imaging (24 papers), AI in cancer detection (7 papers) and Advanced X-ray and CT Imaging (5 papers). Avishek Chatterjee is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (24 papers), AI in cancer detection (7 papers) and Advanced X-ray and CT Imaging (5 papers). Avishek Chatterjee collaborates with scholars based in Netherlands, Canada and United States. Avishek Chatterjee's co-authors include Philippe Lambin, Henry C. Woodruff, Zohaib Salahuddin, Jan Seuntjens, Martin Vallières, Caroline Reinhold, Reza Forghani, Behzad Forghani, George Shenouda and Peter Savadjiev and has published in prestigious journals such as Scientific Reports, Radiology and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

Avishek Chatterjee

37 papers receiving 1.1k citations

Hit Papers

Transparency of deep neural networks for medical image an... 2021 2026 2022 2024 2021 50 100 150 200 250
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. Transparency of deep neural networks for medical image analysis: A review of interpretability methods
    2021 253 citations Zohaib Salahuddin, Henry C. Woodruff et al. Computers in Biology and Medicine profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Avishek Chatterjee Netherlands 17 815 340 283 247 139 38 1.2k
Jonas Teuwen Netherlands 15 962 1.2× 671 2.0× 268 0.9× 216 0.9× 180 1.3× 62 1.4k
Tyler Bradshaw United States 19 1.1k 1.3× 119 0.3× 276 1.0× 372 1.5× 100 0.7× 63 1.4k
Alberto Traverso Netherlands 20 1.4k 1.8× 343 1.0× 482 1.7× 528 2.1× 131 0.9× 68 1.7k
Alireza Mehrtash United States 16 1.2k 1.5× 482 1.4× 443 1.6× 328 1.3× 260 1.9× 32 1.8k
Hidetaka Arimura Japan 19 819 1.0× 245 0.7× 547 1.9× 223 0.9× 43 0.3× 123 1.3k
Chunhao Wang United States 20 653 0.8× 163 0.5× 284 1.0× 217 0.9× 87 0.6× 59 1.1k
Ali Sadeghi‐Naini Canada 28 1.6k 1.9× 672 2.0× 281 1.0× 750 3.0× 61 0.4× 109 2.1k
Zhiguo Zhou China 17 540 0.7× 273 0.8× 269 1.0× 161 0.7× 32 0.2× 36 862
Sachin Jambawalikar United States 26 1.3k 1.6× 469 1.4× 294 1.0× 280 1.1× 74 0.5× 70 1.8k
Lijun Lu China 20 1.1k 1.4× 149 0.4× 358 1.3× 394 1.6× 39 0.3× 83 1.3k

Countries citing papers authored by Avishek Chatterjee

Since Specialization
Citations

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

Fields of papers citing papers by Avishek Chatterjee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Avishek Chatterjee

This figure shows the co-authorship network connecting the top 25 collaborators of Avishek Chatterjee. A scholar is included among the top collaborators of Avishek 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 Avishek Chatterjee. Avishek 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.
Beukinga, Roelof J., Avishek Chatterjee, Henry C. Woodruff, et al.. (2023). External validation of 18F-FDG PET-based radiomic models on identification of residual oesophageal cancer after neoadjuvant chemoradiotherapy. Nuclear Medicine Communications. 44(8). 709–718. 1 indexed citations
2.
Halilaj, Iva, Cary Oberije, Avishek Chatterjee, et al.. (2022). Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology. Biomedicines. 10(11). 2679–2679. 3 indexed citations
3.
Chatterjee, Avishek, et al.. (2022). Improving and Externally Validating Mortality Prediction Models for COVID-19 Using Publicly Available Data. MDPI (MDPI AG). 2(1). 13–26. 4 indexed citations
4.
Esposito, Giovanni, Monique Henket, Marie Winandy, et al.. (2022). AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs. Diagnostics. 12(7). 1608–1608. 4 indexed citations
5.
Lefebvre, Thierry, Yoshiko Ueno, Anthony Dohan, et al.. (2022). Development and Validation of Multiparametric MRI–based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer. Radiology. 305(2). 375–386. 66 indexed citations
6.
Hendriks, Lizza, Simon Keek, Avishek Chatterjee, et al.. (2022). 127P Does radiomics have added value in predicting the development of brain metastases in patients with radically treated stage III non-small cell lung cancer (NSCLC)?. Annals of Oncology. 33. S91–S91. 1 indexed citations
7.
Keek, Simon, Manon Beuque, Sergey Primakov, et al.. (2022). Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics. Frontiers in Oncology. 12. 920393–920393. 8 indexed citations
8.
Granzier, Renée W. Y., Abdalla Ibrahim, Sergey Primakov, et al.. (2021). MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study. Cancers. 13(10). 2447–2447. 26 indexed citations
9.
Chatterjee, Avishek, Cosimo Nardi, Cary Oberije, & Philippe Lambin. (2021). Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape. Journal of Personalized Medicine. 11(4). 300–300. 17 indexed citations
10.
Ibrahim, Abdalla, Henry C. Woodruff, Vincent Andrearczyk, et al.. (2021). Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods. Journal of Personalized Medicine. 11(9). 842–842. 116 indexed citations
11.
12.
Liu, Xiaoyang, Farhad Maleki, Nikesh Muthukrishnan, et al.. (2021). Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models. Cancers. 13(15). 3723–3723. 5 indexed citations
13.
Salahuddin, Zohaib, Henry C. Woodruff, Avishek Chatterjee, & Philippe Lambin. (2021). Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Computers in Biology and Medicine. 140. 105111–105111. 253 indexed citations breakdown →
14.
Chatterjee, Avishek, Henry C. Woodruff, Guangyao Wu, & Philippe Lambin. (2021). Limitations of Only Reporting the Odds Ratio in the Age of Precision Medicine: A Deterministic Simulation Study. Frontiers in Medicine. 8. 640854–640854. 4 indexed citations
15.
Chatterjee, Avishek, Martin Vallières, Anthony Dohan, et al.. (2019). Creating Robust Predictive Radiomic Models for Data From Independent Institutions Using Normalization. IEEE Transactions on Radiation and Plasma Medical Sciences. 3(2). 210–215. 42 indexed citations
16.
Chatterjee, Avishek, et al.. (2019). Deep learning in head & neck cancer outcome prediction. Scientific Reports. 9(1). 2764–2764. 135 indexed citations
17.
Forghani, Reza, Avishek Chatterjee, Caroline Reinhold, et al.. (2019). Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning. European Radiology. 29(11). 6172–6181. 89 indexed citations
18.
Upadhaya, Taman, Martin Vallières, Avishek Chatterjee, et al.. (2018). Comparison of Radiomics Models Built Through Machine Learning in a Multicentric Context With Independent Testing: Identical Data, Similar Algorithms, Different Methodologies. IEEE Transactions on Radiation and Plasma Medical Sciences. 3(2). 192–200. 19 indexed citations
19.
Chatterjee, Avishek, Martin Vallières, Anthony Dohan, et al.. (2018). An Empirical Approach for Avoiding False Discoveries When Applying High-Dimensional Radiomics to Small Datasets. IEEE Transactions on Radiation and Plasma Medical Sciences. 3(2). 201–209. 19 indexed citations
20.
Chatterjee, Avishek, Sergio Faria, Issam El Naqa, et al.. (2018). Can dose outside the PTV influence the risk of distant metastases in stage I lung cancer patients treated with stereotactic body radiotherapy (SBRT)?. Radiotherapy and Oncology. 128(3). 513–519. 19 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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