Michail Mamalakis

479 total citations
19 papers, 245 citations indexed

About

Michail Mamalakis is a scholar working on Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Michail Mamalakis has authored 19 papers receiving a total of 245 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Cardiology and Cardiovascular Medicine and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Michail Mamalakis's work include Cardiac Imaging and Diagnostics (5 papers), COVID-19 diagnosis using AI (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Michail Mamalakis is often cited by papers focused on Cardiac Imaging and Diagnostics (5 papers), COVID-19 diagnosis using AI (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Michail Mamalakis collaborates with scholars based in United Kingdom, Netherlands and United States. Michail Mamalakis's co-authors include Surajit Ray, Abhirup Banerjee, Louise S. Mackenzie, Bart Vorselaars, Mark Baker, Andrew J. Swift, Pankaj Garg, Samer Alabed, Jim M. Wild and Michael Sharkey and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Radiology.

In The Last Decade

Michail Mamalakis

17 papers receiving 239 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michail Mamalakis United Kingdom 9 157 70 55 37 37 19 245
Zhenchao Tang China 5 300 1.9× 116 1.7× 42 0.8× 45 1.2× 42 1.1× 14 398
Victor Savevski Italy 13 202 1.3× 68 1.0× 87 1.6× 36 1.0× 54 1.5× 32 383
Zeno Falaschi Italy 9 158 1.0× 42 0.6× 67 1.2× 33 0.9× 21 0.6× 18 260
Nishanth Arun United States 4 209 1.3× 125 1.8× 28 0.5× 28 0.8× 79 2.1× 7 300
Josef Huemer United Kingdom 11 423 2.7× 28 0.4× 21 0.4× 28 0.8× 42 1.1× 36 608
Shravya Shetty United States 7 167 1.1× 69 1.0× 24 0.4× 35 0.9× 75 2.0× 15 276
Atallah Baydoun United States 12 146 0.9× 60 0.9× 16 0.3× 47 1.3× 16 0.4× 27 364
Youmin Guo China 11 524 3.3× 80 1.1× 69 1.3× 69 1.9× 41 1.1× 33 600
Liyan Pan China 8 96 0.6× 90 1.3× 13 0.2× 10 0.3× 32 0.9× 18 279

Countries citing papers authored by Michail Mamalakis

Since Specialization
Citations

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

Fields of papers citing papers by Michail Mamalakis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michail Mamalakis

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

All Works

19 of 19 papers shown
1.
Mamalakis, Michail, Antonios Mamalakis, Ingrid Agartz, et al.. (2025). Solving the enigma: Enhancing faithfulness and comprehensibility in explanations of deep networks. SHILAP Revista de lepidopterología. 6. 70–81.
2.
Mamalakis, Michail, et al.. (2025). The Explanation Necessity for Healthcare AI. 1–5. 1 indexed citations
3.
Mamalakis, Michail, et al.. (2024). A novel pipeline employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy. Computers in Biology and Medicine. 181. 109052–109052. 1 indexed citations
4.
Dwivedi, Krit, Michael Sharkey, Samer Alabed, et al.. (2024). Improving Prognostication in Pulmonary Hypertension Using AI-quantified Fibrosis and Radiologic Severity Scoring at Baseline CT. Radiology. 310(2). e231718–e231718. 12 indexed citations
5.
Shen, Yiqing, Zan Chen, Michail Mamalakis, et al.. (2024). A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding. 2390–2395. 6 indexed citations
6.
Mamalakis, Michail, Abhirup Banerjee, Surajit Ray, et al.. (2024). Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning. Neural Computing and Applications. 36(30). 18841–18862.
7.
Mamalakis, Michail, Pankaj Garg, Tom Nelson, et al.. (2023). Artificial Intelligence framework with traditional computer vision and deep learning approaches for optimal automatic segmentation of left ventricle with scar. Artificial Intelligence in Medicine. 143. 102610–102610. 9 indexed citations
8.
Mamalakis, Michail, Samer Alabed, Rob J. van der Geest, et al.. (2023). Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease. SHILAP Revista de lepidopterología. 11(1). 20–20. 15 indexed citations
9.
Mamalakis, Michail, Krit Dwivedi, Michael Sharkey, et al.. (2023). A transparent artificial intelligence framework to assess lung disease in pulmonary hypertension. Scientific Reports. 13(1). 3812–3812. 12 indexed citations
10.
Ragnhildstveit, Anya, et al.. (2023). Intra-operative applications of augmented reality in glioma surgery: a systematic review. Frontiers in Surgery. 10. 1245851–1245851. 10 indexed citations
11.
Alabed, Samer, Michael Sharkey, Michail Mamalakis, et al.. (2022). Quality of reporting in AI cardiac MRI segmentation studies – A systematic review and recommendations for future studies. Frontiers in Cardiovascular Medicine. 9. 956811–956811. 17 indexed citations
12.
Banerjee, Abhirup, Michail Mamalakis, Surajit Ray, et al.. (2022). Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers. International Journal of Molecular Sciences. 23(13). 7260–7260. 4 indexed citations
13.
Ray, Surajit, Abhirup Banerjee, Andrew J. Swift, et al.. (2022). A robust COVID-19 mortality prediction calculator based on Lymphocyte count, Urea, C-Reactive Protein, Age and Sex (LUCAS) with chest X-rays. Scientific Reports. 12(1). 18220–18220. 4 indexed citations
14.
Mamalakis, Michail, Pankaj Garg, Tom Nelson, et al.. (2022). Automatic development of 3D anatomical models of border zone and core scar regions in the left ventricle. Computerized Medical Imaging and Graphics. 103. 102152–102152. 4 indexed citations
15.
Alabed, Samer, Asad Mahmood, Michael Sharkey, et al.. (2022). The quality of reporting in cardiac MRI artificial intelligence segmentation studies - a systematic review. European Heart Journal - Cardiovascular Imaging. 23(Supplement_2). 1 indexed citations
16.
Sharkey, Michael, Jonathan Taylor, Samer Alabed, et al.. (2022). Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning. Frontiers in Cardiovascular Medicine. 9. 983859–983859. 17 indexed citations
17.
Mamalakis, Michail, Pankaj Garg, Tom Nelson, et al.. (2021). MA-SOCRATIS: An automatic pipeline for robust segmentation of the left ventricle and scar. Computerized Medical Imaging and Graphics. 93. 101982–101982. 8 indexed citations
18.
Banerjee, Abhirup, Surajit Ray, Bart Vorselaars, et al.. (2020). Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population. International Immunopharmacology. 86. 106705–106705. 120 indexed citations
19.
Michalopoulos, Nikolaos V., et al.. (2016). A Personalised Monitoring and Recommendation Framework for Kinetic Dysfunctions. 1–4. 4 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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