Pushpak Pati

587 total citations
20 papers, 237 citations indexed

About

Pushpak Pati is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Pushpak Pati has authored 20 papers receiving a total of 237 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Pushpak Pati's work include AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Cell Image Analysis Techniques (5 papers). Pushpak Pati is often cited by papers focused on AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Cell Image Analysis Techniques (5 papers). Pushpak Pati collaborates with scholars based in Switzerland, United States and India. Pushpak Pati's co-authors include Maria Gabrani, Orçun Göksel, Guillaume Jaume, Raúl Catena, Antonio Foncubierta–Rodríguez, Thomas Binder, Giosuè Scognamiglio, Anna Fomitcheva Khartchenko, Nadia Brancati and Daniel Riccio and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Medical Imaging and Medical Image Analysis.

In The Last Decade

Pushpak Pati

20 papers receiving 234 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pushpak Pati Switzerland 9 166 101 80 33 31 20 237
Niccolò Marini Switzerland 9 157 0.9× 102 1.0× 74 0.9× 27 0.8× 30 1.0× 20 213
Zhaoyang Xu China 6 164 1.0× 133 1.3× 66 0.8× 27 0.8× 17 0.5× 16 221
Quoc Dang Vu United Kingdom 7 166 1.0× 130 1.3× 99 1.2× 35 1.1× 38 1.2× 11 249
Navid Alemi Koohbanani United Kingdom 4 159 1.0× 116 1.1× 91 1.1× 36 1.1× 22 0.7× 5 227
Baochuan Pang China 8 228 1.4× 105 1.0× 130 1.6× 26 0.8× 32 1.0× 31 323
Trevor Manz United States 4 140 0.8× 92 0.9× 62 0.8× 62 1.9× 17 0.5× 10 251
Jesper Molin Sweden 8 242 1.5× 123 1.2× 68 0.8× 63 1.9× 30 1.0× 18 318
Mason McGough United States 3 151 0.9× 84 0.8× 55 0.7× 28 0.8× 12 0.4× 4 196
M. Milagro Fernández-Carrobles Spain 11 206 1.2× 86 0.9× 122 1.5× 69 2.1× 17 0.5× 18 315
Karsten Wendt Germany 8 96 0.6× 65 0.6× 99 1.2× 20 0.6× 18 0.6× 16 267

Countries citing papers authored by Pushpak Pati

Since Specialization
Citations

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

Fields of papers citing papers by Pushpak Pati

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pushpak Pati

This figure shows the co-authorship network connecting the top 25 collaborators of Pushpak Pati. A scholar is included among the top collaborators of Pushpak Pati 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 Pushpak Pati. Pushpak Pati 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.
Pati, Pushpak, Sofia Karkampouna, Francesco Bonollo, et al.. (2024). Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling. Nature Machine Intelligence. 6(9). 1077–1093. 21 indexed citations
2.
3.
Pati, Pushpak, Srijan Das, Maria Vakalopoulou, et al.. (2024). SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology. PubMed. 2024. 11226–11237. 6 indexed citations
4.
Şahin, Mehmet, Benjamin C. B. Symons, Pushpak Pati, et al.. (2024). Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training. Quantum. 8. 1502–1502. 4 indexed citations
5.
Pati, Pushpak, et al.. (2024). Hybrid Quantum-Classical Graph Neural Networks for Tumor Classification in Digital Pathology. 1611–1616. 1 indexed citations
6.
Gupta, Rajarsi, et al.. (2024). Multi-Scale Feature Alignment for Continual Learning of Unlabeled Domains. IEEE Transactions on Medical Imaging. 43(7). 2599–2609. 3 indexed citations
7.
Pati, Pushpak, et al.. (2023). Weakly supervised joint whole-slide segmentation and classification in prostate cancer. Medical Image Analysis. 89. 102915–102915. 17 indexed citations
8.
Pati, Pushpak, et al.. (2023). Matching single cells across modalities with contrastive learning and optimal transport. Briefings in Bioinformatics. 24(3). 4 indexed citations
9.
Pati, Pushpak, et al.. (2023). Generative appearance replay for continual unsupervised domain adaptation. Medical Image Analysis. 89. 102924–102924. 10 indexed citations
10.
Brancati, Nadia, Anna Maria Anniciello, Pushpak Pati, et al.. (2022). BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images. Database. 2022. 65 indexed citations
11.
Pati, Pushpak, Guillaume Jaume, Florinda Feroce, et al.. (2021). Hierarchical Cell-to-Tissue Graph Representations for Breast Cancer Subtyping in Digital Pathology.. arXiv (Cornell University). 3 indexed citations
12.
Pati, Pushpak, Antonio Foncubierta–Rodríguez, Orçun Göksel, & Maria Gabrani. (2020). Reducing annotation effort in digital pathology: A Co-Representation learning framework for classification tasks. Medical Image Analysis. 67. 101859–101859. 25 indexed citations
13.
Kashyap, Aditya, Anna Fomitcheva Khartchenko, Pushpak Pati, et al.. (2019). Quantitative microimmunohistochemistry for the grading of immunostains on tumour tissues. Nature Biomedical Engineering. 3(6). 478–490. 24 indexed citations
14.
Binder, Thomas, et al.. (2019). Multi-Organ Gland Segmentation Using Deep Learning. Frontiers in Medicine. 6. 173–173. 30 indexed citations
15.
Pati, Pushpak, Aditya Kashyap, Anna Fomitcheva Khartchenko, et al.. (2019). High-Quality Immunohistochemical Stains Through Computational Assay Parameter Optimization. IEEE Transactions on Biomedical Engineering. 66(10). 2952–2963. 8 indexed citations
16.
Pati, Pushpak, Raúl Catena, Orçun Göksel, & Maria Gabrani. (2019). A deep learning framework for context-aware mitotic activity estimation in whole slide images. 4. 7–7. 2 indexed citations
17.
Pati, Pushpak, Matheus P. Viana, Maria Gabrani, et al.. (2018). Deep positive-unlabeled learning for region of interest localization in breast tissue images. 9420. 3–3. 2 indexed citations
18.
Pati, Pushpak, et al.. (2016). FPGA implementation of rule optimization for stand-alone tunable fuzzy logic controller using GA. Complex & Intelligent Systems. 2(2). 83–98. 9 indexed citations
19.
Jaju, Santosh, et al.. (2013). A Review on FEM Analysis of Mandibular Overdenture Implant. International Journal of Innovative Research in Science Engineering and Technology. 2(1). 2137–2144. 1 indexed citations
20.
Pati, Pushpak, et al.. (2013). âÂÂA REVIEW ON APPLICATION OF CAD AND FEM TECHNOLOGY IN DESIGN OFTAPER DENTAL IMPLANTâÂÂ. International Journal of Innovative Research in Science Engineering and Technology. 2(6). 2091–2096. 1 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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