Massimo Salvi

2.2k total citations · 2 hit papers
85 papers, 1.3k citations indexed

About

Massimo Salvi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Massimo Salvi has authored 85 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 24 papers in Computer Vision and Pattern Recognition and 22 papers in Biomedical Engineering. Recurrent topics in Massimo Salvi's work include AI in cancer detection (24 papers), Cell Image Analysis Techniques (11 papers) and Digital Imaging for Blood Diseases (8 papers). Massimo Salvi is often cited by papers focused on AI in cancer detection (24 papers), Cell Image Analysis Techniques (11 papers) and Digital Imaging for Blood Diseases (8 papers). Massimo Salvi collaborates with scholars based in Italy, Australia and Singapore. Massimo Salvi's co-authors include Filippo Molinari, U. Rajendra Acharya, Kristen M. Meiburger, Silvia Seoni, Prabal Datta Barua, Nicola Michielli, Luca Molinaro, Mauro Papotti, Jahmunah Vicnesh and Alessandro Gambella and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Journal of Hepatology.

In The Last Decade

Massimo Salvi

71 papers receiving 1.3k 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.

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

2020 242 citations Massimo Salvi, U. Rajendra Acharya et al. profile →
Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)

2023 97 citations Silvia Seoni, Massimo Salvi et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Massimo Salvi Italy	21	559	442	324	202	136	85	1.3k
Ching‐Wei Wang Taiwan	23	567 1.0×	402 0.9×	333 1.0×	326 1.6×	133 1.0×	82	1.8k
Rajarsi Gupta United States	18	468 0.8×	643 1.5×	210 0.6×	288 1.4×	155 1.1×	51	1.3k
Claes Lundström Sweden	20	545 1.0×	444 1.0×	518 1.6×	128 0.6×	83 0.6×	64	1.3k
Shadi Albarqouni Germany	17	1.0k 1.8×	706 1.6×	577 1.8×	161 0.8×	123 0.9×	49	1.6k
Maximilian Baust Germany	14	743 1.3×	499 1.1×	637 2.0×	186 0.9×	96 0.7×	33	1.5k
Veronika Cheplygina Netherlands	14	593 1.1×	381 0.9×	464 1.4×	111 0.5×	85 0.6×	24	1.3k
Keerthana Prasad India	21	516 0.9×	489 1.1×	512 1.6×	107 0.5×	74 0.5×	74	1.3k
Diana Mateus France	20	431 0.8×	492 1.1×	683 2.1×	259 1.3×	112 0.8×	63	1.5k
Carmelo Militello Italy	27	326 0.6×	417 0.9×	499 1.5×	250 1.2×	154 1.1×	126	1.9k
Yuchen Qiu United States	18	859 1.5×	1.1k 2.5×	331 1.0×	238 1.2×	116 0.9×	62	1.8k

Countries citing papers authored by Massimo Salvi

Since Specialization

Citations

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

Fields of papers citing papers by Massimo Salvi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Massimo Salvi

This figure shows the co-authorship network connecting the top 25 collaborators of Massimo Salvi. A scholar is included among the top collaborators of Massimo Salvi 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 Massimo Salvi. Massimo Salvi 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

Salvi, Massimo, Silvia Seoni, Andrea Campagner, et al.. (2025). Explainability and uncertainty: Two sides of the same coin for enhancing the interpretability of deep learning models in healthcare. International Journal of Medical Informatics. 197. 105846–105846. 14 indexed citations

Atıla, Orhan, et al.. (2025). A novel uncertainty-aware liquid neural network for noise-resilient time series forecasting and classification. Chaos Solitons & Fractals. 193. 116130–116130. 3 indexed citations

Hafeez‐Baig, Abdul, et al.. (2025). FlexiCombFE: A flexible, combination-based feature engineering framework for brain tumor detection. Biomedical Signal Processing and Control. 104. 107538–107538. 3 indexed citations

Sciancalepore, Francesco, Roberta Balestrino, Iva Stanković, et al.. (2025). Multi-Center 3D CNN for Parkinson’s disease diagnosis and prognosis using clinical and T1-weighted MRI data. NeuroImage Clinical. 48. 103859–103859.

Torre, Carolina de la, et al.. (2025). A cascade approach for the early detection and localization of myocardial infarction in 2D-echocardiography. Medical Engineering & Physics. 143(1). 104400–104400.

Gudigar, Anjan, U. Raghavendra, Massimo Salvi, et al.. (2025). A Dual-Stream Deep Learning Architecture With Adaptive Random Vector Functional Link for Multi-Center Ischemic Stroke Classification. IEEE Access. 13. 46638–46658. 2 indexed citations

Salvi, Massimo, et al.. (2025). Deep learning-driven glioblastoma diagnosis from histopathological images via single-cell segmentation and morphological analysis. Machine Learning Science and Technology. 6(4). 45052–45052.

Chakraborty, Subrata, Massimo Salvi, Oliver Faust, et al.. (2025). Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications. IEEE Access. 13. 25026–25044.

Chakraborty, Subrata, Silvia Seoni, Massimo Salvi, et al.. (2024). Artificial Intelligence-Based Suicide Prevention and Prediction: A Systematic Review (2019-2023). SSRN Electronic Journal. 3 indexed citations

10.

Salvi, Massimo, Nicola Michielli, Alessandro Gambella, et al.. (2024). Computational Synthesis of Histological Stains: A Step Toward Virtual Enhanced Digital Pathology. International Journal of Imaging Systems and Technology. 34(5). 1 indexed citations

11.

Salvi, Massimo, Silvia Seoni, Oliver Faust, et al.. (2024). Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023). Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 14(3). 6 indexed citations

12.

Eppinga, Wietse S.C., et al.. (2024). Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation. Physics in Medicine and Biology. 69(3). 35007–35007. 12 indexed citations

13.

Meiburger, Kristen M., et al.. (2023). Impact of artificial intelligence‐based color constancy on dermoscopical assessment of skin lesions: A comparative study. Skin Research and Technology. 29(11). e13508–e13508. 7 indexed citations

14.

Raghavendra, U., Anjan Gudigar, Massimo Salvi, et al.. (2023). A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images. IEEE Access. 11. 108982–108994. 7 indexed citations

15.

Gambella, Alessandro, Massimo Salvi, Luca Molinaro, et al.. (2023). Improved assessment of donor liver steatosis using Banff consensus recommendations and deep learning algorithms. Journal of Hepatology. 80(3). 495–504. 6 indexed citations

16.

Raghavendra, U., Anjan Gudigar, Ajay Hegde, et al.. (2023). YOLOv5s-CAM: A Deep Learning Model for Automated Detection and Classification for Types of Intracranial Hematoma in CT Images. IEEE Access. 11. 141309–141328. 5 indexed citations

17.

Salvi, Massimo, et al.. (2023). cyto‐Knet: An instance segmentation approach for multiple myeloma plasma cells using conditional kernels. International Journal of Imaging Systems and Technology. 34(1). 5 indexed citations

18.

Salvi, Massimo, Alessandro Caputo, Davide Balmativola, et al.. (2023). Impact of Stain Normalization on Pathologist Assessment of Prostate Cancer: A Comparative Study. Cancers. 15(5). 1503–1503. 14 indexed citations

19.

Salvi, Massimo, Martino Bosco, Luca Molinaro, et al.. (2021). A hybrid deep learning approach for gland segmentation in prostate histopathological images. Artificial Intelligence in Medicine. 115. 102076–102076. 49 indexed citations

20.

Salvi, Massimo, Kristen M. Meiburger, Alessandro Gambella, et al.. (2020). Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys. Electronics. 9(10). 1644–1644. 24 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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