S Spasov

645 total citations · 1 hit paper
9 papers, 359 citations indexed

About

S Spasov is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, S Spasov has authored 9 papers receiving a total of 359 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Artificial Intelligence and 3 papers in Cognitive Neuroscience. Recurrent topics in S Spasov's work include Functional Brain Connectivity Studies (3 papers), Advanced Neuroimaging Techniques and Applications (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). S Spasov is often cited by papers focused on Functional Brain Connectivity Studies (3 papers), Advanced Neuroimaging Techniques and Applications (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). S Spasov collaborates with scholars based in Italy, United Kingdom and United States. S Spasov's co-authors include Píetro Lió, Andrea Duggento, Nicola Toschi, Luca Passamonti, Giovanna Maria Dimitri, Carlo Maj, Tiago Azevedo, Ivan Merelli and Oleg Borisov and has published in prestigious journals such as NeuroImage, Information Fusion and Image and Vision Computing.

In The Last Decade

S Spasov

8 papers receiving 354 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.

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease

2019 281 citations S Spasov, Luca Passamonti et al. NeuroImage profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
S Spasov Italy	5	146	136	131	77	68	9	359
Ramesh Kumar Lama South Korea	10	111 0.8×	140 1.0×	89 0.7×	73 0.9×	83 1.2×	22	391
Akshay Pai Denmark	8	140 1.0×	109 0.8×	95 0.7×	89 1.2×	70 1.0×	28	356
Yonggui Yang China	7	103 0.7×	144 1.1×	111 0.8×	111 1.4×	56 0.8×	16	355
Yubraj Gupta Germany	8	135 0.9×	125 0.9×	82 0.6×	63 0.8×	70 1.0×	19	312
ADNI ADNI United States	7	180 1.2×	257 1.9×	179 1.4×	116 1.5×	94 1.4×	11	501
Tory O. Frizzell Canada	7	129 0.9×	161 1.2×	101 0.8×	141 1.8×	146 2.1×	8	418
Domenico Diacono Italy	14	110 0.8×	104 0.8×	186 1.4×	169 2.2×	122 1.8×	38	579
Kanghan Oh South Korea	9	100 0.7×	140 1.0×	157 1.2×	94 1.2×	96 1.4×	19	461
Michael Kawczynski United States	5	105 0.7×	111 0.8×	142 1.1×	232 3.0×	38 0.6×	10	486
Farheen Ramzan Pakistan	5	69 0.5×	180 1.3×	136 1.0×	89 1.2×	56 0.8×	7	387

Countries citing papers authored by S Spasov

Since Specialization

Citations

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

Fields of papers citing papers by S Spasov

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S Spasov

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

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Duggento, Andrea, et al.. (2023). Contrastive learning for unsupervised medical image clustering and reconstruction. 4. 2 indexed citations

2.

Spasov, S, et al.. (2023). VAESim: A probabilistic approach for self-supervised prototype discovery. Image and Vision Computing. 137. 104746–104746. 5 indexed citations

3.

Dimitri, Giovanna Maria, S Spasov, Andrea Duggento, et al.. (2022). Multimodal and multicontrast image fusion via deep generative models. Information Fusion. 88. 146–160. 31 indexed citations

4.

Dimitri, Giovanna Maria, S Spasov, Andrea Duggento, et al.. (2020). Unsupervised stratification in neuroimaging through deep latent embeddings. PubMed. 2020. 1568–1571. 16 indexed citations

5.

Maj, Carlo, Tiago Azevedo, Oleg Borisov, et al.. (2019). Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer’s Disease. Frontiers in Genetics. 10. 726–726. 21 indexed citations

6.

Spasov, S, Luca Passamonti, Andrea Duggento, Píetro Lió, & Nicola Toschi. (2019). A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. NeuroImage. 189. 276–287. 281 indexed citations breakdown →

7.

Spasov, S, et al.. (1981). [FSH, LH, estradiol and testosterone studies of the blood serum in endometriosis externa].. PubMed. 20(4). 312–5. 1 indexed citations

8.

Spasov, S, et al.. (1979). [Effect of gasoline vapors on the hormonal activity of the ovaries].. PubMed. 18(6). 430–2. 1 indexed citations

9.

Spasov, S, et al.. (1973). [Association of gonadal dysgenesis and diabetes insipidus].. PubMed. 12(6). 497–500. 1 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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