Damian Podareanu

507 total citations
15 papers, 180 citations indexed

About

Damian Podareanu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Damian Podareanu has authored 15 papers receiving a total of 180 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Damian Podareanu's work include Digital Imaging for Blood Diseases (4 papers), AI in cancer detection (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Damian Podareanu is often cited by papers focused on Digital Imaging for Blood Diseases (4 papers), AI in cancer detection (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Damian Podareanu collaborates with scholars based in Netherlands, United States and Switzerland. Damian Podareanu's co-authors include Caspar M. van Leeuwen, Chiel C. van Heerwaarden, Rob Verheyen, Roberto Ruiz de Austri, Melissa van Beekveld, Sydney Otten, S. Caron, Luc Hendriks, Robert Pincus and Mart van Rijthoven and has published in prestigious journals such as Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, Medical Image Analysis and Future Generation Computer Systems.

In The Last Decade

Damian Podareanu

13 papers receiving 172 citations

Peers

Countries citing papers authored by Damian Podareanu

Since Specialization

Citations

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

Fields of papers citing papers by Damian Podareanu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Damian Podareanu

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

All Works

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15 of 15 papers shown

#	Work	Indexed citations
1	Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning 2024 ·Medical Image Analysis ·Niccolò Marini, Stefano Marchesin, Marek Wodziński, Alessandro Caputo, Damian Podareanu, (unknown), Svetla Boytcheva, (unknown), Filippo Fraggetta, Francesco Ciompi, Gianmaria Silvello, Henning Müller, Manfredo Atzori	4
2	Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis 2024 ·VU Research Portal ·Xiaoyu Chu, (unknown), Shashikant Ilager, (unknown), (unknown), Damian Podareanu, Dmitry Duplyakin, Ivona Brandić, Alexandru Iosup	3
3	Improving the speed and quality of cancer segmentation using lower resolution pathology images 2023 ·Multimedia Tools and Applications ·(unknown), (unknown), (unknown), Stevan Rudinac, (unknown), Damian Podareanu	2
4	DIANNA: Deep Insight And Neural NetworkAnalysis 2022 ·The Journal of Open Source Software ·(unknown), Christiaan Meijer, L. C. Oostrum, (unknown), (unknown), (unknown), M. Laneuville, (unknown), Rena Bakhshi, Damian Podareanu	0
5	Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations 2022 ·npj Digital Medicine ·Niccolò Marini, Stefano Marchesin, Sebastian Otálora, Marek Wodziński, Alessandro Caputo, Mart van Rijthoven, (unknown), John‐Melle Bokhorst, Damian Podareanu, (unknown), Svetla Boytcheva, (unknown), (unknown), Filippo Fraggetta, Jeroen van der Laak, Maristella Agosti, Francesco Ciompi, Gianmaria Silvello, Henning Müller, Manfredo Atzori	40
6	Less is not more: We need rich datasets to explore 2022 ·Future Generation Computer Systems ·Laurens Versluis, (unknown), (unknown), (unknown), Damian Podareanu, (unknown), Alexandru Uta, Alexandru Iosup	3
7	2021 ·UvA-DARE (University of Amsterdam) ·Sydney Otten, S. Caron, (unknown), Melissa van Beekveld, Luc Hendriks, Caspar M. van Leeuwen, Damian Podareanu, Roberto Ruiz de Austri, Rob Verheyen	53
8	Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow 2021 ·Geoscientific model development ·(unknown), Caspar M. van Leeuwen, Damian Podareanu, (unknown), (unknown), Martin Janssens, Oscar Hartogensis, Chiel C. van Heerwaarden	16
9	Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images 2021 ·Frontiers in Computer Science ·Niccolò Marini, Sebastian Otálora, Damian Podareanu, Mart van Rijthoven, Jeroen van der Laak, Francesco Ciompi, Henning Müller, Manfredo Atzori	14
10	Predicting atmospheric optical properties for radiative transfer computations using neural networks 2021 ·Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences ·(unknown), Robert Pincus, (unknown), Caspar M. van Leeuwen, Damian Podareanu, Chiel C. van Heerwaarden	28
11	Predicting atmospheric optical properties for radiative transfer computations using neural networks 2020 ·(unknown), Robert Pincus, Caspar M. van Leeuwen, Damian Podareanu, (unknown), Chiel C. van Heerwaarden	3
12	Generative Adversarial Networks for Fast Simulation: distributed training and generalisation 2020 ·Federico Carminati, S. Vallecorsa, (unknown), (unknown), Damian Podareanu, Maxwell Xu Cai, Vikram A. Saletore, Hans Pabst	3
13	Event Generation and Statistical Sampling with Deep Generative Models and a Density Information Buffer 2019 ·arXiv (Cornell University) ·Sydney Otten, S. Caron, (unknown), Melissa van Beekveld, Luc Hendriks, Caspar M. van Leeuwen, Damian Podareanu, Roberto Ruiz de Austri, Rob Verheyen	2
14	Event Generation and Statistical Sampling with Deep Generative Models 2019 ·arXiv (Cornell University) ·Sydney Otten, Luc Hendriks, Melissa van Beekveld, Caspar M. van Leeuwen, Rob Verheyen, Roberto Ruiz de Austri, Damian Podareanu, (unknown), S. Caron	0
15	Large Minibatch Training on Supercomputers with Improved Accuracy and Reduced Time to Train 2018 ·(unknown), Damian Podareanu, Vikram A. Saletore	9

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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