Maria Colomba Comes
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 5%
- Biomedical Engineering
- Oncology
- Pulmonary and Respiratory Medicine
- Co-authors
- Eugenio MartinelliArianna MencattiniPaola CastiAnnarita FanizziRaffaella MassafraDavide Di GiuseppeCorrado Di NataleSamantha Bove
- Topics
- Radiomics and Machine Learning in Medical Imaging (22 papers)AI in cancer detection (16 papers)Cell Image Analysis Techniques (14 papers)
In The Last Decade
Maria Colomba Comes
52 papers receiving 682 citations
Peers
Comparison fields: 5 of 109
- Radiology, Nuclear Medicine and Imaging 274
- Artificial Intelligence 226
- Biomedical Engineering 194
- Oncology 100
- Pulmonary and Respiratory Medicine 92
Countries citing papers authored by Maria Colomba Comes
This map shows the geographic impact of Maria Colomba Comes'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 Maria Colomba Comes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Colomba Comes more than expected).
Fields of papers citing papers by Maria Colomba Comes
This network shows the impact of papers produced by Maria Colomba Comes. 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 Maria Colomba Comes. The network helps show where Maria Colomba Comes may publish in the future.
Co-authorship network of co-authors of Maria Colomba Comes
This figure shows the co-authorship network connecting the top 25 collaborators of Maria Colomba Comes. A scholar is included among the top collaborators of Maria Colomba Comes 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 Maria Colomba Comes. Maria Colomba Comes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 8 | |
| 6 | 6 | |
| 7 | 0 | |
| 8 | 0 | |
| 9 | 18 | |
| 10 | 8 | |
| 11 | 5 | |
| 12 | 5 | |
| 13 | 11 | |
| 14 | 4 | |
| 15 | 5 | |
| 16 | 22 | |
| 17 | 9 | |
| 18 | ADMM-DIPTV: combining Total Variation and Deep Image Prior for image restoration. | 6 |
| 19 | 48 | |
| 20 | 29 |
About Maria Colomba Comes
Maria Colomba Comes is a scholar working on Biophysics, Radiology, Nuclear Medicine and Imaging and Leadership and Management, having authored 60 papers that have together received 691 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (22 papers), AI in cancer detection (16 papers) and Cell Image Analysis Techniques (14 papers). The work is most often cited by research in Biophysics (89 citations), Radiology, Nuclear Medicine and Imaging (274 citations) and Health Informatics (15 citations). Maria Colomba Comes has collaborated with scholars based in Italy, France and Türkiye. Frequent co-authors include Eugenio Martinelli, Arianna Mencattini, Paola Casti, Annarita Fanizzi, Raffaella Massafra, Davide Di Giuseppe, Corrado Di Natale, Samantha Bove, Daniele La Forgia and Pasquale Tamborra. Their work appears in journals such as Bioinformatics, PLoS ONE and Scientific Reports.
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.