Pietro Antonio Cicalese

487 total citations
8 papers, 265 citations indexed

About

Pietro Antonio Cicalese is a scholar working on Nephrology, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Pietro Antonio Cicalese has authored 8 papers receiving a total of 265 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Nephrology, 3 papers in Pulmonary and Respiratory Medicine and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Pietro Antonio Cicalese's work include Renal and Vascular Pathologies (2 papers), Systemic Lupus Erythematosus Research (2 papers) and Advanced Neural Network Applications (2 papers). Pietro Antonio Cicalese is often cited by papers focused on Renal and Vascular Pathologies (2 papers), Systemic Lupus Erythematosus Research (2 papers) and Advanced Neural Network Applications (2 papers). Pietro Antonio Cicalese collaborates with scholars based in United States, Germany and China. Pietro Antonio Cicalese's co-authors include Chandra Mohan, Hien Van Nguyen, Chushan Wang, Sudhakar Selvaraj, Rihui Li, Joseph T. Francis, Yingchun Zhang, Paul E. Schulz, Badrinath Roysam and Angela Ernst and has published in prestigious journals such as Kidney International, Annals of the Rheumatic Diseases and IEEE Transactions on Medical Imaging.

In The Last Decade

Pietro Antonio Cicalese

8 papers receiving 261 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pietro Antonio Cicalese United States	6	83	65	48	48	41	8	265
Zoulikha Malek France	8	61 0.7×	30 0.5×	114 2.4×	93 1.9×	24 0.6×	9	391
Asif Mazumder United Kingdom	9	63 0.8×	34 0.5×	24 0.5×	6 0.1×	4 0.1×	17	334
Daniele Della Latta Italy	13	203 2.4×	22 0.3×	39 0.8×	6 0.1×	17 0.4×	34	451
Zhongli Chen China	10	41 0.5×	67 1.0×	29 0.6×	23 0.5×	18 0.4×	38	411
Jihong Fang China	7	16 0.2×	13 0.2×	12 0.3×	16 0.3×	14 0.3×	10	162
Jesse Wei United States	11	183 2.2×	30 0.5×	231 4.8×	16 0.3×	7 0.2×	21	455
Gillian Macnaught United Kingdom	11	148 1.8×	18 0.3×	49 1.0×	6 0.1×	12 0.3×	29	350
Ajay Hegde India	12	52 0.6×	11 0.2×	29 0.6×	5 0.1×	8 0.2×	55	375
Weiwei Quan China	9	37 0.4×	11 0.2×	57 1.2×	11 0.2×	10 0.2×	32	292
James Lee United States	9	32 0.4×	31 0.5×	18 0.4×	5 0.1×	5 0.1×	27	288

Countries citing papers authored by Pietro Antonio Cicalese

Since Specialization

Citations

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

Fields of papers citing papers by Pietro Antonio Cicalese

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pietro Antonio Cicalese. 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 Pietro Antonio Cicalese. The network helps show where Pietro Antonio Cicalese may publish in the future.

Co-authorship network of co-authors of Pietro Antonio Cicalese

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

All Works

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8 of 8 papers shown

Mobiny, Aryan, Pietro Antonio Cicalese, Naveen Garg, et al.. (2021). Memory-Augmented Capsule Network for Adaptable Lung Nodule Classification. IEEE Transactions on Medical Imaging. 40(10). 2869–2879. 21 indexed citations

Vanarsa, Kamala, Ting Zhang, Sanam Soomro, et al.. (2021). Comprehensive aptamer-based screen of 1317 proteins uncovers improved stool protein markers of colorectal cancer. Journal of Gastroenterology. 56(7). 659–672. 16 indexed citations

Mobiny, Aryan, Pietro Antonio Cicalese, & Hien Van Nguyen. (2021). Trans-Caps: Transformer Capsule Networks with Self-attention Routing. 1 indexed citations

Lutnick, Brendon, Surya V. Seshan, Jesper Kers, et al.. (2021). MO077AUTOMATIC SEGMENTATION OF ARTERIES, ARTERIOLES AND GLOMERULI IN NATIVE BIOPSIES WITH THROMBOTIC MICROANGIOPATHY AND OTHER VASCULAR DISEASES. Nephrology Dialysis Transplantation. 36(Supplement_1). 1 indexed citations

Cicalese, Pietro Antonio, Rihui Li, Chushan Wang, et al.. (2020). An EEG-fNIRS hybridization technique in the four-class classification of alzheimer’s disease. Journal of Neuroscience Methods. 336. 108618–108618. 85 indexed citations

Cicalese, Pietro Antonio, et al.. (2020). Kidney Level Lupus Nephritis Classification Using Uncertainty Guided Bayesian Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics. 25(2). 315–324. 22 indexed citations

Vanarsa, Kamala, Sanam Soomro, Ting Zhang, et al.. (2020). Quantitative planar array screen of 1000 proteins uncovers novel urinary protein biomarkers of lupus nephritis. Annals of the Rheumatic Diseases. 79(10). 1349–1361. 49 indexed citations

Becker, Jan U., David Mayerich, Jonathan Barratt, et al.. (2020). Artificial intelligence and machine learning in nephropathology. Kidney International. 98(1). 65–75. 70 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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