Lisa Di Jorio

About

Lisa Di Jorio is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Lisa Di Jorio has authored 5 papers receiving a total of 280 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Computer Vision and Pattern Recognition, 2 papers in Biomedical Engineering and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Lisa Di Jorio’s work include Multimodal Machine Learning Applications (2 papers), Video Analysis and Summarization (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). Lisa Di Jorio is often cited by papers focused on Multimodal Machine Learning Applications (2 papers), Video Analysis and Summarization (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). Lisa Di Jorio collaborates with scholars based in Canada, United Kingdom and France. Lisa Di Jorio's co-authors include Samuel Kadoury, An Tang, Eugene Vorontsov, Yoshua Bengio, Mahsa Shakeri, Adriana Romero, Michal Drozdzal, Gabriel Chartrand, Chris Pal and Réal Lapointe and has published in prestigious journals such as Medical Image Analysis, Radiology Artificial Intelligence and HAL (Le Centre pour la Communication Scientifique Directe).

In The Last Decade

Co-authorship network of co-authors of Lisa Di Jorio i

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Fields of papers citing papers by Lisa Di Jorio

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lisa Di Jorio. 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 Lisa Di Jorio. The network helps show where Lisa Di Jorio may publish in the future.

Countries citing papers authored by Lisa Di Jorio

Since Specialization

Citations

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

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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