Pritam Mukherjee

900 total citations
39 papers, 484 citations indexed

About

Pritam Mukherjee is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Pritam Mukherjee has authored 39 papers receiving a total of 484 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Radiology, Nuclear Medicine and Imaging, 19 papers in Artificial Intelligence and 10 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Pritam Mukherjee's work include Radiomics and Machine Learning in Medical Imaging (18 papers), AI in cancer detection (9 papers) and Topic Modeling (7 papers). Pritam Mukherjee is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (18 papers), AI in cancer detection (9 papers) and Topic Modeling (7 papers). Pritam Mukherjee collaborates with scholars based in United States, Belgium and India. Pritam Mukherjee's co-authors include Olivier Gevaert, Ronald M. Summers, Benjamin Hou, Mu Zhou, Sandra Steyaert, Hannes Vogel, Yuanning Zheng, Sandy Napel, Chao Huang and Vincent Vandecaveye and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cancer Research and Scientific Reports.

In The Last Decade

Pritam Mukherjee

32 papers receiving 478 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Pritam Mukherjee 316 149 89 88 80 39 484
Siri Willems 342 1.1× 158 1.1× 49 0.6× 97 1.1× 75 0.9× 14 616
Dooman Arefan 418 1.3× 273 1.8× 80 0.9× 138 1.6× 42 0.5× 43 597
Ge-Ge Wu 479 1.5× 318 2.1× 51 0.6× 93 1.1× 103 1.3× 10 710
Seyed Masoud Rezaeijo 406 1.3× 174 1.2× 62 0.7× 163 1.9× 24 0.3× 31 603
Arash Mohtashamian 312 1.0× 386 2.6× 63 0.7× 120 1.4× 118 1.5× 11 567
Sunan Cui 356 1.1× 158 1.1× 37 0.4× 133 1.5× 92 1.1× 18 574
Christoph Haarburger 446 1.4× 310 2.1× 48 0.5× 77 0.9× 80 1.0× 16 676
Lily H. Peng 376 1.2× 446 3.0× 93 1.0× 134 1.5× 129 1.6× 6 689
Niels Olson 370 1.2× 467 3.1× 83 0.9× 132 1.5× 137 1.7× 9 734

Countries citing papers authored by Pritam Mukherjee

Since Specialization
Citations

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

Fields of papers citing papers by Pritam Mukherjee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pritam Mukherjee

This figure shows the co-authorship network connecting the top 25 collaborators of Pritam Mukherjee. A scholar is included among the top collaborators of Pritam Mukherjee 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 Pritam Mukherjee. Pritam Mukherjee 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.
Hou, Benjamin, et al.. (2025). One Year On: Assessing Progress of Multimodal Large Language Model Performance on RSNA 2024 Case of the Day Questions. Radiology. 316(2). e250617–e250617. 1 indexed citations
2.
Liu, Jianfei, et al.. (2025). A unified approach to medical image segmentation by leveraging mixed supervision and self and transfer learning (MIST). Computerized Medical Imaging and Graphics. 122. 102517–102517. 1 indexed citations
3.
4.
Mukherjee, Pritam, Jianfei Liu, Abhishek Jha, et al.. (2024). Weakly supervised detection of pheochromocytomas and paragangliomas in CT using noisy data. Computerized Medical Imaging and Graphics. 116. 102419–102419.
5.
Zhuang, Yan, et al.. (2024). Segmentation of pelvic structures in T2 MRI via MR-to-CT synthesis. Computerized Medical Imaging and Graphics. 112. 102335–102335. 3 indexed citations
6.
Mukherjee, Pritam, Benjamin Hou, Yan Zhuang, et al.. (2024). Evaluation of GPT Large Language Model Performance on RSNA 2023 Case of the Day Questions. Radiology. 313(1). e240609–e240609. 8 indexed citations
7.
Kim, Boah, et al.. (2024). Automated classification of body MRI sequence type using convolutional neural networks. 17–17. 3 indexed citations
8.
Kim, Boah, et al.. (2024). Automated Classification of Body MRI Sequences Using Convolutional Neural Networks. Academic Radiology. 32(3). 1192–1203. 1 indexed citations
9.
10.
Jin, Qiao, Benjamin Hou, Pritam Mukherjee, et al.. (2024). Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for AI-generated Radiology Reports. PubMed. 2024. 402–411. 4 indexed citations
11.
Kiffer, Frederico, Daniel C. Berrios, Sylvain V. Costes, et al.. (2023). Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium. International Journal of Radiation Biology. 99(8). 1291–1300. 4 indexed citations
12.
Selby, Heather M., Pritam Mukherjee, Sachin B. Malik, et al.. (2023). Performance of alternative manual and automated deep learning segmentation techniques for the prediction of benign and malignant lung nodules. Journal of Medical Imaging. 10(4). 44006–44006.
13.
Zhu, Qingqing, et al.. (2023). Utilizing Longitudinal Chest X-Rays and Reports to Pre-fill Radiology Reports. Lecture notes in computer science. 14224. 189–198. 11 indexed citations
14.
Mukherjee, Pritam, et al.. (2023). Anatomical Location-Guided Deep Learning-Based Genetic Cluster Identification of Pheochromocytomas and Paragangliomas from CT Images. Lecture notes in computer science. 14313. 62–71. 1 indexed citations
15.
Mukherjee, Pritam, et al.. (2023). SCOPE: predicting future diagnoses in office visits using electronic health records. Scientific Reports. 13(1). 11005–11005. 1 indexed citations
16.
Venkataraman, Vivek, et al.. (2022). ImaGene: a web-based software platform for tumor radiogenomic evaluation and reporting. Bioinformatics Advances. 2(1). vbac079–vbac079. 6 indexed citations
17.
Mukherjee, Pritam, et al.. (2022). Topological data analysis of thoracic radiographic images shows improved radiomics-based lung tumor histology prediction. Patterns. 4(1). 100657–100657. 12 indexed citations
18.
19.
Mukherjee, Pritam, Mu Zhou, Edward Lee, et al.. (2020). A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets. Nature Machine Intelligence. 2(5). 274–282. 66 indexed citations
20.
Mukherjee, Pritam, Chao Huang, Mu Zhou, et al.. (2020). CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma. Radiology Imaging Cancer. 2(3). e190039–e190039. 48 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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