Niccolò Marini

422 total citations
20 papers, 213 citations indexed

About

Niccolò Marini is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Niccolò Marini has authored 20 papers receiving a total of 213 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 11 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Niccolò Marini's work include AI in cancer detection (13 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Digital Imaging for Blood Diseases (8 papers). Niccolò Marini is often cited by papers focused on AI in cancer detection (13 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Digital Imaging for Blood Diseases (8 papers). Niccolò Marini collaborates with scholars based in Switzerland, Italy and Netherlands. Niccolò Marini's co-authors include Henning Müller, Manfredo Atzori, Sebastian Otálora, Francesco Ciompi, Marek Wodziński, Gianmaria Silvello, Stéphane Marchand‐Maillet, Mart van Rijthoven, Stefano Marchesin and Jeroen van der Laak and has published in prestigious journals such as Sensors, Medical Image Analysis and Computer Methods and Programs in Biomedicine.

In The Last Decade

Niccolò Marini

18 papers receiving 211 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Niccolò Marini Switzerland 9 157 102 74 30 27 20 213
Quoc Dang Vu United Kingdom 7 166 1.1× 130 1.3× 99 1.3× 38 1.3× 35 1.3× 11 249
Pushpak Pati Switzerland 9 166 1.1× 101 1.0× 80 1.1× 31 1.0× 12 0.4× 20 237
Julio Silva-Rodríguez Spain 8 171 1.1× 105 1.0× 84 1.1× 28 0.9× 31 1.1× 14 220
Mason McGough United States 3 151 1.0× 84 0.8× 55 0.7× 12 0.4× 19 0.7× 4 196
Ruining Deng United States 10 152 1.0× 98 1.0× 128 1.7× 24 0.8× 39 1.4× 42 302
Jesper Molin Sweden 8 242 1.5× 123 1.2× 68 0.9× 30 1.0× 27 1.0× 18 318
Can Koyuncu United States 10 123 0.8× 106 1.0× 69 0.9× 19 0.6× 30 1.1× 21 253
Maxime W. Lafarge Netherlands 9 55 0.4× 97 1.0× 86 1.2× 31 1.0× 20 0.7× 11 211
Deepak Anand India 7 111 0.7× 108 1.1× 36 0.5× 20 0.7× 13 0.5× 15 199
Ivy Liang United States 2 228 1.5× 161 1.6× 72 1.0× 18 0.6× 26 1.0× 3 385

Countries citing papers authored by Niccolò Marini

Since Specialization
Citations

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

Fields of papers citing papers by Niccolò Marini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Niccolò Marini

This figure shows the co-authorship network connecting the top 25 collaborators of Niccolò Marini. A scholar is included among the top collaborators of Niccolò Marini 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 Niccolò Marini. Niccolò Marini 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.
Marini, Niccolò, et al.. (2025). Green AI: Which Programming Language Consumes the Most?. Florence Research (University of Florence). 12–19. 1 indexed citations
2.
Rajaraman, Sivaramakrishnan, Zhaohui Liang, Niccolò Marini, Zhiyun Xue, & Sameer Antani. (2025). The Hidden Threat of Hallucinations in Binary Chest X-Ray Pneumonia Classification. PubMed. 2025. 668–673.
3.
Marini, Niccolò, et al.. (2024). A Multi-Scale CNN for Transfer Learning in sEMG-Based Hand Gesture Recognition for Prosthetic Devices. Sensors. 24(22). 7147–7147. 9 indexed citations
4.
Dominguez‐Morales, Juan P., Lourdes Durán-López, Niccolò Marini, et al.. (2024). A systematic comparison of deep learning methods for Gleason grading and scoring. Medical Image Analysis. 95. 103191–103191. 4 indexed citations
5.
Marini, Niccolò, Stefano Marchesin, Marek Wodziński, et al.. (2024). Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning. Medical Image Analysis. 97. 103303–103303. 4 indexed citations
6.
Marini, Niccolò, Simona Vatrano, Irıs D. Nagtegaal, et al.. (2024). Automated classification of celiac disease in histopathological images: a multi-scale approach. Universitätsbibliographie, Universität Duisburg-Essen. 7. 86–86.
7.
Wodziński, Marek, Niccolò Marini, Manfredo Atzori, & Henning Müller. (2024). RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge. Computer Methods and Programs in Biomedicine. 250. 108187–108187. 5 indexed citations
8.
Marini, Niccolò, Simona Vatrano, Alessandro Caputo, et al.. (2024). A full pipeline to analyze lung histopathology images. ArODES (HES-SO (https://www.hes-so.ch/)). 2–2. 1 indexed citations
9.
Marini, Niccolò, Sebastian Otálora, Marek Wodziński, et al.. (2023). Data-driven color augmentation for H&E stained images in computational pathology. Journal of Pathology Informatics. 14. 100183–100183. 24 indexed citations
10.
Tomassini, Selene, Nicola Falcionelli, Giulia Bruschi, et al.. (2023). On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans. Computerized Medical Imaging and Graphics. 110. 102310–102310. 9 indexed citations
11.
Silvello, Gianmaria, Manfredo Atzori, Svetla Boytcheva, et al.. (2023). Modelling digital health data: The ExaMode ontology for computational pathology. Journal of Pathology Informatics. 14. 100332–100332. 2 indexed citations
12.
Marini, Niccolò, Stefano Marchesin, Sebastian Otálora, et al.. (2022). Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations. npj Digital Medicine. 5(1). 102–102. 40 indexed citations
13.
Marchesin, Stefano, Niccolò Marini, Manfredo Atzori, et al.. (2022). Empowering digital pathology applications through explainable knowledge extraction tools. Journal of Pathology Informatics. 13. 100139–100139. 10 indexed citations
14.
Marini, Niccolò, Marek Wodziński, Manfredo Atzori, & Henning Müller. (2022). A Multi-Task Multiple Instance Learning Algorithm to Analyze Large Whole Slide Images from Bright Challenge 2022. ArODES (HES-SO (https://www.hes-so.ch/)). 1–4. 3 indexed citations
15.
Marini, Niccolò, Lora Fanda, Yazan Mualla, et al.. (2022). A DEXiRE for Extracting Propositional Rules from Neural Networks via Binarization. Electronics. 11(24). 4171–4171. 3 indexed citations
16.
Marini, Niccolò, Sebastian Otálora, Henning Müller, & Manfredo Atzori. (2021). Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification. Medical Image Analysis. 73. 102165–102165. 49 indexed citations
17.
Otálora, Sebastian, Niccolò Marini, Henning Müller, & Manfredo Atzori. (2021). Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification. BMC Medical Imaging. 21(1). 77–77. 21 indexed citations
18.
Marini, Niccolò, Sebastian Otálora, Francesco Ciompi, et al.. (2021). Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations. ArODES (HES-SO (https://www.hes-so.ch/)). 6 indexed citations
19.
Marini, Niccolò, Manfredo Atzori, Sebastian Otálora, Stéphane Marchand‐Maillet, & Henning Müller. (2021). H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression. ArODES (HES-SO (https://www.hes-so.ch/)). 601–610. 8 indexed citations
20.
Marini, Niccolò, Sebastian Otálora, Damian Podareanu, et al.. (2021). Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images. Frontiers in Computer Science. 3. 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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