Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
In The Last Decade
doi.org/10.1109/tbme.2014.2372011 →Countries where authors are citing Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
This map shows the geographic impact of Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease. 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 Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease more than expected).
Fields of papers citing Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
This network shows the impact of Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease.
About Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
This paper, published in 2014, received 404 indexed citations . Written by Siqi Liu, Sidong Liu, Weidong Cai, Sonia Pujol, Ron Kikinis, Dagan Feng, Michael Fulham and ADNI ADNI covering the research area of Molecular Biology, Artificial Intelligence and Psychiatry and Mental health. It is primarily cited by scholars working on Neurology (230 citations), Artificial Intelligence (149 citations) and Psychiatry and Mental health (139 citations). Published in IEEE Transactions on Biomedical Engineering.
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.
This paper is also available at doi.org/10.1109/tbme.2014.2372011.