Gabriel Maicas

495 total citations
9 papers, 133 citations indexed

About

Gabriel Maicas is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Genetics. According to data from OpenAlex, Gabriel Maicas has authored 9 papers receiving a total of 133 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Radiology, Nuclear Medicine and Imaging and 2 papers in Genetics. Recurrent topics in Gabriel Maicas's work include Radiomics and Machine Learning in Medical Imaging (4 papers), Medical Image Segmentation Techniques (3 papers) and Artificial Intelligence in Healthcare and Education (2 papers). Gabriel Maicas is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (4 papers), Medical Image Segmentation Techniques (3 papers) and Artificial Intelligence in Healthcare and Education (2 papers). Gabriel Maicas collaborates with scholars based in Australia, Japan and South Africa. Gabriel Maicas's co-authors include Gustavo Carneiro, Andrew P. Bradley, Warren Seow, Hidde M. Kroon, Tarik Sammour, Sergei Bedrikovetski, Nagendra N. Dudi‐Venkata, James W. Moore, Leonardo Zorrón Cheng Tao Pu and Takeshi Yamamura and has published in prestigious journals such as IEEE Access, Gastrointestinal Endoscopy and Artificial Intelligence in Medicine.

In The Last Decade

Gabriel Maicas

9 papers receiving 129 citations

Peers

Countries citing papers authored by Gabriel Maicas

Since Specialization

Citations

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

Fields of papers citing papers by Gabriel Maicas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gabriel Maicas

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

All Works

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9 of 9 papers shown

#	Work	Indexed citations
1	What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning 2023 ·Melissa D. McCradden, (unknown), Shalmali Joshi, (unknown), Kagiso Ndlovu, Ben Glocker, Gabriel Maicas, Xiaoxuan Liu, Mjaye Mazwi, (unknown), Lauren Oakden‐Rayner, Myrtede Alfred, (unknown), (unknown), Anna Goldenberg	11
2	Mutual Information Neural Estimation for Unsupervised Multi-Modal Registration of Brain Images 2022 ·2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) ·(unknown), Michele Sasdelli, Gabriel Maicas, Stephan Lau, Johan Verjans, Mark Jenkinson, Gustavo Carneiro	4
3	Post-hoc overall survival time prediction from brain MRI 2021 ·Adelaide Research & Scholarship (AR&S) (University of Adelaide) ·(unknown), Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneiro	4
4	Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: A systematic review and meta-analysis 2021 ·Artificial Intelligence in Medicine ·Sergei Bedrikovetski, Nagendra N. Dudi‐Venkata, Gabriel Maicas, Hidde M. Kroon, Warren Seow, Gustavo Carneiro, James W. Moore, Tarik Sammour	32
5	Photoshopping Colonoscopy Video Frames 2020 ·Adelaide Research & Scholarship (AR&S) (University of Adelaide) ·Yuyuan Liu, Yu Tian, Gabriel Maicas, Leonardo Zorrón Cheng Tao Pu, Rajvinder Singh, Johan Verjans, Gustavo Carneiro	1
6	Computer-aided diagnosis for characterization of colorectal lesions: comprehensive software that includes differentiation of serrated lesions 2020 ·Gastrointestinal Endoscopy ·Leonardo Zorrón Cheng Tao Pu, Gabriel Maicas, Yu Tian, Takeshi Yamamura, Masanao Nakamura, Hiroto Suzuki, (unknown), (unknown), Yoshiki Hirooka, Alastair D. Burt, Mitsuhiro Fujishiro, Gustavo Carneiro, Rajvinder Singh	39
7	Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning 2020 ·IEEE Access ·(unknown), Yu Takeda, Fengbei Liu, Fumio Sasazawa, Gabriel Maicas, Ross Crawford, Jonathan Roberts, Ajay K. Pandey, Gustavo Carneiro	18
8	Model agnostic saliency for weakly supervised lesion detection from breast DCE-MRI 2019 ·Adelaide Research & Scholarship (AR&S) (University of Adelaide) ·Gabriel Maicas, (unknown), Andrew P. Bradley, Ian Reid, Gustavo Carneiro	7
9	Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior 2017 ·Adelaide Research & Scholarship (AR&S) (University of Adelaide) ·Gabriel Maicas, Gustavo Carneiro, Andrew P. Bradley	17

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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