Filippo Arcadu

731 total citations · 1 hit paper
20 papers, 464 citations indexed

About

Filippo Arcadu is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation and Ophthalmology. According to data from OpenAlex, Filippo Arcadu has authored 20 papers receiving a total of 464 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Radiation and 5 papers in Ophthalmology. Recurrent topics in Filippo Arcadu's work include Advanced X-ray Imaging Techniques (8 papers), Retinal Imaging and Analysis (7 papers) and Retinal Diseases and Treatments (5 papers). Filippo Arcadu is often cited by papers focused on Advanced X-ray Imaging Techniques (8 papers), Retinal Imaging and Analysis (7 papers) and Retinal Diseases and Treatments (5 papers). Filippo Arcadu collaborates with scholars based in Switzerland, United States and Sweden. Filippo Arcadu's co-authors include Marco Prunotto, Andreas Maunz, Fethallah Benmansour, Jeffrey R. Willis, Zdenka Hašková, Marco Stampanoni, Faye Drawnel, Young S. Oh, Rajmund Mokso and Dana McClintock and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Image Processing.

In The Last Decade

Filippo Arcadu

18 papers receiving 449 citations

Hit Papers

align trajectories

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.

Deep learning algorithm predicts diabetic retinopathy progression in individual patients

2019 188 citations Filippo Arcadu, Fethallah Benmansour et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Filippo Arcadu Switzerland	10	263	127	62	60	51	20	464
Georgios C. Manikis Greece	15	489 1.9×	132 1.0×	97 1.6×	136 2.3×	3 0.1×	65	795
Meysam Tavakoli United States	11	273 1.0×	179 1.4×	12 0.2×	24 0.4×	7 0.1×	42	375
Erwin Bellon Belgium	9	423 1.6×	23 0.2×	40 0.6×	38 0.6×	248 4.9×	30	699
Nathaniel Swinburne United States	10	223 0.8×	12 0.1×	27 0.4×	113 1.9×	7 0.1×	18	512
Mishka Gidwani United States	5	315 1.2×	9 0.1×	65 1.0×	143 2.4×	30 0.6×	7	480
Margaret Pain United States	15	203 0.8×	19 0.1×	83 1.3×	147 2.5×	2 0.0×	31	800
Marwa Ismail United States	9	319 1.2×	77 0.6×	32 0.5×	52 0.9×	1 0.0×	33	419
Sang-Woo Lee South Korea	13	348 1.3×	47 0.4×	60 1.0×	22 0.4×	7 0.1×	27	498
Mohamed Shehata Egypt	14	377 1.4×	19 0.1×	23 0.4×	152 2.5×	3 0.1×	55	648
T. Y. Alvin Liu United States	13	369 1.4×	361 2.8×	15 0.2×	52 0.9×		57	598

Countries citing papers authored by Filippo Arcadu

Since Specialization

Citations

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

Fields of papers citing papers by Filippo Arcadu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Filippo Arcadu

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

All Works

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20 of 20 papers shown

Nalepa, Jakub, et al.. (2023). Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients. Computers in Biology and Medicine. 154. 106603–106603. 16 indexed citations

Arcadu, Filippo, et al.. (2022). MMO-Net (Multi-Magnification Organ Network): A use case for Organ Identification using Multiple Magnifications in Preclinical Pathology Studies. Journal of Pathology Informatics. 13. 100126–100126. 3 indexed citations

Arcadu, Filippo, et al.. (2021). Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. SHILAP Revista de lepidopterología. 14. 3246595551–3246595551. 47 indexed citations

Maunz, Andreas, Fethallah Benmansour, Thomas Albrecht, et al.. (2021). Accuracy of a Machine-Learning Algorithm for Detecting and Classifying Choroidal Neovascularization on Spectral-Domain Optical Coherence Tomography. Journal of Personalized Medicine. 11(6). 524–524. 6 indexed citations

Maunz, Andreas, Fethallah Benmansour, Yun Li, et al.. (2020). Diagnostic accuracy of a machine-learning algorithm to detect and classify choroidal neovascularization based on SD-OCT in neovascular age-related macular degeneration (nAMD). Investigative Ophthalmology & Visual Science. 61(7). 2649–2649.

Arcadu, Filippo, Fethallah Benmansour, Andreas Maunz, et al.. (2020). Author Correction: Deep learning algorithm predicts diabetic retinopathy progression in individual patients. npj Digital Medicine. 3(1). 160–160. 10 indexed citations

Oh, Young S., et al.. (2020). P212 Deep learning video analysis for a fully automated per-frame grading of ulcerative colitis. Journal of Crohn s and Colitis. 14(Supplement_1). S246–S247. 1 indexed citations

Sahni, Jayashree, et al.. (2019). A machine learning approach to predict response to anti-VEGF treatment in patients with neovascular age-related macular degeneration using SD-OCT. Investigative Ophthalmology & Visual Science. 60(11). 1 indexed citations

Arcadu, Filippo, Fethallah Benmansour, Andreas Maunz, et al.. (2019). Deep learning algorithm for patient-level prediction of diabetic retinopathy (DR) response to vascular endothelial growth factor (VEGF) inhibition. Investigative Ophthalmology & Visual Science. 60(9). 2806–2806. 2 indexed citations

10.

Moll, Solange, Alexis Desmoulière, Marcus J. Moeller, et al.. (2019). DDR1 role in fibrosis and its pharmacological targeting. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research. 1866(11). 118474–118474. 64 indexed citations

11.

Arcadu, Filippo, Fethallah Benmansour, Andreas Maunz, et al.. (2019). Deep learning algorithm predicts diabetic retinopathy progression in individual patients. npj Digital Medicine. 2(1). 92–92. 188 indexed citations breakdown →

12.

Arcadu, Filippo, Fethallah Benmansour, Andreas Maunz, et al.. (2019). Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs. Investigative Ophthalmology & Visual Science. 60(4). 852–852. 50 indexed citations

13.

Lovrić, Goran, Rajmund Mokso, Filippo Arcadu, et al.. (2017). Tomographic in vivo microscopy for the study of lung physiology at the alveolar level. Scientific Reports. 7(1). 12545–12545. 29 indexed citations

14.

Oikonomidis, Ioannis Vogiatzis, Tiziana P. Cremona, Goran Lovrić, et al.. (2017). Effective segmentation of fresh post-mortem murine lung parenchyma in phase contrast X-ray tomographic microscopy images. Journal of Physics Conference Series. 849. 12006–12006. 2 indexed citations

15.

Villanueva‐Perez, Pablo, Filippo Arcadu, Peter Cloetens, & Marco Stampanoni. (2017). Contrast-transfer-function phase retrieval based on compressed sensing. Optics Letters. 42(6). 1133–1133. 10 indexed citations

16.

Oikonomidis, Ioannis Vogiatzis, Goran Lovrić, Tiziana P. Cremona, et al.. (2017). Imaging samples larger than the field of view: the SLS experience. Journal of Physics Conference Series. 849. 12004–12004. 7 indexed citations

17.

Villanueva‐Perez, Pablo, Bill Pedrini, Rajmund Mokso, et al.. (2016). Signal-to-noise criterion for free-propagation imaging techniques at free-electron lasers and synchrotrons. Optics Express. 24(4). 3189–3189. 12 indexed citations

18.

Arcadu, Filippo, Federica Marone, & Marco Stampanoni. (2016). Fast iterative reconstruction of data in full interior tomography. Journal of Synchrotron Radiation. 24(1). 205–219. 5 indexed citations

19.

Arcadu, Filippo, Marco Stampanoni, & Federica Marone. (2016). Fast gridding projectors for analytical and iterative tomographic reconstruction of differential phase contrast data. Optics Express. 24(13). 14748–14748.

20.

Arcadu, Filippo, et al.. (2016). A Forward Regridding Method With Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms. IEEE Transactions on Image Processing. 25(3). 1207–1218. 11 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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