Donato Cascio

1.1k total citations
48 papers, 664 citations indexed

About

Donato Cascio is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Donato Cascio has authored 48 papers receiving a total of 664 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 18 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Computer Vision and Pattern Recognition. Recurrent topics in Donato Cascio's work include AI in cancer detection (19 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Systemic Lupus Erythematosus Research (8 papers). Donato Cascio is often cited by papers focused on AI in cancer detection (19 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Systemic Lupus Erythematosus Research (8 papers). Donato Cascio collaborates with scholars based in Italy, United Kingdom and United States. Donato Cascio's co-authors include G. Raso, F. Fauci, R. Magro, Sabina Tangaro, S.C. Cheran, Alessandra Retico, R. Bellotti, Francesco De Carlo, L. Abbene and Bruno Golosio and has published in prestigious journals such as PLoS ONE, IEEE Access and Sensors.

In The Last Decade

Donato Cascio

42 papers receiving 623 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Donato Cascio Italy 15 323 259 199 196 78 48 664
Guy Nir Canada 14 501 1.6× 438 1.7× 177 0.9× 307 1.6× 26 0.3× 26 966
Shichong Zhou China 18 813 2.5× 597 2.3× 229 1.2× 130 0.7× 10 0.1× 63 1.4k
Yuanzhi Cheng China 13 253 0.8× 138 0.5× 260 1.3× 100 0.5× 50 0.6× 48 572
Pingjun Chen United States 11 239 0.7× 284 1.1× 176 0.9× 29 0.1× 145 1.9× 23 629
Shujun Wang China 10 407 1.3× 306 1.2× 263 1.3× 36 0.2× 10 0.1× 30 694
Iringo Kovacs Netherlands 5 386 1.2× 538 2.1× 181 0.9× 109 0.6× 7 0.1× 7 762
Hussain Fatakdawala United States 13 200 0.6× 177 0.7× 135 0.7× 64 0.3× 21 0.3× 19 525
Dániel Tóth United Kingdom 14 160 0.5× 32 0.1× 156 0.8× 91 0.5× 17 0.2× 49 573
Xuxin Chen United States 11 400 1.2× 307 1.2× 157 0.8× 129 0.7× 6 0.1× 40 858
Krzysztof J. Geras United States 11 569 1.8× 531 2.1× 84 0.4× 153 0.8× 81 1.0× 28 907

Countries citing papers authored by Donato Cascio

Since Specialization
Citations

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

Fields of papers citing papers by Donato Cascio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Donato Cascio

This figure shows the co-authorship network connecting the top 25 collaborators of Donato Cascio. A scholar is included among the top collaborators of Donato Cascio 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 Donato Cascio. Donato Cascio 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.
Conte, Luana, Giorgio De Nunzio, G. Raso, & Donato Cascio. (2025). Multi-Class Segmentation and Classification of Intestinal Organoids: YOLO Stand-Alone vs. Hybrid Machine Learning Pipelines. Applied Sciences. 15(21). 11311–11311.
2.
3.
Conte, Luana, Gerardo Caruso, Anil K. Philip, et al.. (2025). Artificial Intelligence-Assisted Drug and Biomarker Discovery for Glioblastoma: A Scoping Review of the Literature. Cancers. 17(4). 571–571. 8 indexed citations
4.
Cocchi, Enrico, Valeria Cremonini, Elsa Vitale, et al.. (2025). Pediatric Pain Management: An Observational Study on Nurses’ Knowledge of Non-Pharmacological Techniques. Nursing Reports. 15(8). 290–290.
5.
Conte, Luana, Ilaria Amodeo, Giorgio De Nunzio, et al.. (2025). A machine learning approach to predict mortality and neonatal persistent pulmonary hypertension in newborns with congenital diaphragmatic hernia. A retrospective observational cohort study. European Journal of Pediatrics. 184(4). 238–238. 2 indexed citations
6.
Conte, Luana, Giorgio De Nunzio, Roberto Lupo, et al.. (2024). Raising awareness may increase the likelihood of hematopoietic stem cell donation: a nationwide survey using artificial intelligence. International Journal of Hematology. 121(4). 511–525.
7.
Raso, G., Vito Gentile, L. Abbene, et al.. (2023). Automated Stabilization, Enhancement and Capillaries Segmentation in Videocapillaroscopy. Sensors. 23(18). 7674–7674. 6 indexed citations
8.
Principato, F., G. Gerardi, Donato Cascio, et al.. (2023). Window-Based Energy Selecting X-ray Imaging and Charge Sharing in Cadmium Zinc Telluride Linear Array Detectors for Contaminant Detection. Sensors. 23(6). 3196–3196. 2 indexed citations
9.
Tabacchi, Marco Elio, Domenico Tegolo, Donato Cascio, et al.. (2022). A Fuzzy-Based Clinical Decision Support System for Coeliac Disease. IEEE Access. 10. 102223–102236. 6 indexed citations
10.
Amodeo, Ilaria, Giorgio De Nunzio, Genny Raffaeli, et al.. (2021). A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. PLoS ONE. 16(11). e0259724–e0259724. 13 indexed citations
11.
Cascio, Donato, et al.. (2020). HEp-2 Intensity Classification based on Deep Fine-tuning. 143–149. 2 indexed citations
12.
Abbene, L., F. Principato, G. Gerardi, et al.. (2020). Room-temperature X-ray response of cadmium–zinc–telluride pixel detectors grown by the vertical Bridgman technique. Journal of Synchrotron Radiation. 27(2). 319–328. 23 indexed citations
13.
Cascio, Donato, et al.. (2019). A Wavelet approach to extract main features from indirect immunofluorescence images. Nova Science Publishers (Nova Science Publishers, Inc.). 180–187. 1 indexed citations
14.
Abbene, L., F. Principato, G. Gerardi, et al.. (2018). Charge loss correction in CZT pixel detectors at low and high fluxes: analysis of positive and negative pulses. Nova Science Publishers (Nova Science Publishers, Inc.). 1 indexed citations
15.
Cascio, Donato, et al.. (2014). Mammographic images segmentation based on chaotic map clustering algorithm. BMC Medical Imaging. 14(1). 12–12. 15 indexed citations
16.
Cascio, Donato, et al.. (2014). Fuzzy technique for microcalcifications clustering in digital mammograms. BMC Medical Imaging. 14(1). 23–23. 27 indexed citations
17.
Ciatto, Stefano, Donato Cascio, F. Fauci, et al.. (2009). Computer-assisted diagnosis (CAD) in mammography: comparison of diagnostic accuracy of a new algorithm (Cyclopus®, Medicad) with two commercial systems. La radiologia medica. 114(4). 626–635. 16 indexed citations
18.
Bellotti, R., Francesco De Carlo, G. Gargano, et al.. (2007). A CAD system for nodule detection in low‐dose lung CTs based on region growing and a new active contour model. Medical Physics. 34(12). 4901–4910. 88 indexed citations
19.
Bottigli, U., Bruno Golosio, Giovanni Luca Masala, et al.. (2005). DISSIMILARITY APPLICATION FOR MEDICAL IMAGING CLASSIFICATION. Nova Science Publishers (Nova Science Publishers, Inc.). 2 indexed citations
20.
Masala, Giovanni Luca, Bruno Golosio, P. Oliva, et al.. (2005). “Classifiers Trained on dissimilarity representation of medical pattern : A comparative study”. Kent Academic Repository (University of Kent). 68. 905–912. 14 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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