Francesco Renna

2.5k total citations · 2 hit papers
75 papers, 1.5k citations indexed

About

Francesco Renna is a scholar working on Pulmonary and Respiratory Medicine, Signal Processing and Computer Networks and Communications. According to data from OpenAlex, Francesco Renna has authored 75 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Pulmonary and Respiratory Medicine, 24 papers in Signal Processing and 19 papers in Computer Networks and Communications. Recurrent topics in Francesco Renna's work include Phonocardiography and Auscultation Techniques (21 papers), Sparse and Compressive Sensing Techniques (18 papers) and Wireless Communication Security Techniques (12 papers). Francesco Renna is often cited by papers focused on Phonocardiography and Auscultation Techniques (21 papers), Sparse and Compressive Sensing Techniques (18 papers) and Wireless Communication Security Techniques (12 papers). Francesco Renna collaborates with scholars based in Portugal, United Kingdom and United States. Francesco Renna's co-authors include Anders C. Hansen, Clarice Poon, Vegard Antun, Ben Adcock, Miguel Coimbra, Jorge Oliveira, Nicola Laurenti, Miguel R. D. Rodrigues, H. Vincent Poor and Irina B. Grafova and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Gut.

In The Last Decade

Francesco Renna

70 papers receiving 1.4k citations

Hit Papers

On instabilities of deep learning in image reconstruction... 2020 2026 2022 2024 2020 2021 100 200 300 400
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. On instabilities of deep learning in image reconstruction and the potential costs of AI
    2020 414 citations Vegard Antun, Francesco Renna et al. Proceedings of the National Academy of Sciences profile →
  2. The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification
    2021 119 citations Jorge Oliveira, Francesco Renna et al. IEEE Journal of Biomedical and Health Informatics profile →

Peers

Francesco Renna
Ping Lü China
L. W. Nolte United States
Muhammad Salman Khan Pakistan
Marc Garbey United States
N.M. Allinson United Kingdom
Leslie S. Smith United Kingdom
Gustavo M. Callicó Spain
Roshan M. D’Souza United States
M.J.T. Smith United States
Ping Lü China
Francesco Renna
Citations per year, relative to Francesco Renna Francesco Renna (= 1×) peers Ping Lü

Countries citing papers authored by Francesco Renna

Since Specialization
Citations

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

Fields of papers citing papers by Francesco Renna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Francesco Renna

This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Renna. A scholar is included among the top collaborators of Francesco Renna 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 Francesco Renna. Francesco Renna 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.
Filipe, Vítor, et al.. (2024). Lightweight 3D CNN for the Segmentation of Coronary Calcifications and Calcium Scoring. 346–351. 2 indexed citations
2.
3.
Elola, Andoni, Elisabete Aramendi, Jorge Oliveira, et al.. (2023). Beyond Heart Murmur Detection: Automatic Murmur Grading From Phonocardiogram. IEEE Journal of Biomedical and Health Informatics. 27(8). 3856–3866. 13 indexed citations
4.
Coimbra, Miguel, et al.. (2023). Cross-Domain Detection of Pulmonary Hypertension in Human and Porcine Heart Sounds. Computing in cardiology.
5.
Monteiro-Silva, Filipe, et al.. (2023). On the Impact of Synchronous Electrocardiogram Signals for Heart Sounds Segmentation. PubMed. 2023. 1–5. 1 indexed citations
6.
Reyna, Matthew A., Yashar Kiarashi, Andoni Elola, et al.. (2023). Heart murmur detection from phonocardiogram recordings: The George B. Moody PhysioNet Challenge 2022. SHILAP Revista de lepidopterología. 2(9). e0000324–e0000324. 41 indexed citations
7.
8.
Kiarashi, Yashar, Andoni Elola, Jorge Oliveira, et al.. (2022). Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022. Computing in cardiology. 49. 7 indexed citations
9.
Oliveira, Jorge, Francesco Renna, Paulo Dias Costa, et al.. (2021). The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification. IEEE Journal of Biomedical and Health Informatics. 26(6). 2524–2535. 119 indexed citations breakdown →
10.
Ferreira, André C., Francesco Renna, Hanja B. Brandl, et al.. (2020). Deep learning‐based methods for individual recognition in small birds. Methods in Ecology and Evolution. 11(9). 1072–1085. 121 indexed citations
11.
Sokolić, Jure, et al.. (2020). Accurate, Very Low Computational Complexity Spike Sorting Using Unsupervised Matched Subspace Learning. IEEE Transactions on Biomedical Circuits and Systems. 14(2). 221–231. 18 indexed citations
12.
Arribas, Julia, Giulio Antonelli, Leonardo Frazzoni, et al.. (2020). Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis. Gut. 70(8). 1458–1468. 50 indexed citations
13.
Renna, Francesco, et al.. (2020). Source Separation With Side Information Based on Gaussian Mixture Models With Application in Art Investigation. IEEE Transactions on Signal Processing. 68. 558–572. 10 indexed citations
14.
Antun, Vegard, Francesco Renna, Clarice Poon, Ben Adcock, & Anders C. Hansen. (2020). On instabilities of deep learning in image reconstruction and the potential costs of AI. Proceedings of the National Academy of Sciences. 117(48). 30088–30095. 414 indexed citations breakdown →
15.
Oliveira, Jorge, et al.. (2019). Using Soft Attention Mechanisms to Classify Heart Sounds. PubMed. 133. 6669–6672. 1 indexed citations
16.
Oliveira, Jorge, et al.. (2017). On modifying the temporal modeling of HSMMs for pediatric heart sound segmentation. 39. 1–6. 4 indexed citations
17.
Renna, Francesco, et al.. (2016). On the design of linear projections for compressive sensing with side information. 670–674. 4 indexed citations
18.
Renna, Francesco, et al.. (2016). Bounds on the Number of Measurements for Reliable Compressive Classification. IEEE Transactions on Signal Processing. 64(22). 5778–5793. 13 indexed citations
19.
Renna, Francesco, et al.. (2016). Signal reconstruction in the presence of side information: The impact of projection kernel design. 21. 4618–4622. 2 indexed citations
20.
Renna, Francesco, et al.. (2013). Power allocation strategies for OFDM Gaussian wiretap channels with a friendly jammer. 5. 3413–3417. 8 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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