Loss odyssey in medical image segmentation

397 indexed citations
published 2021

Impact in

Classified as

Authors
Jun MaJianan ChenMatthew NgChen LiXiaoping Yang
Journal
Medical Image Analysis

In The Last Decade

doi.org/10.1016/j.media.2021.102035 →

Countries where authors are citing Loss odyssey in medical image segmentation

Specialization
Citations

This map shows the geographic impact of Loss odyssey in medical image segmentation. 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 Loss odyssey in medical image segmentation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Loss odyssey in medical image segmentation more than expected).

Fields of papers citing Loss odyssey in medical image segmentation

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Loss odyssey in medical image segmentation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Loss odyssey in medical image segmentation.

About Loss odyssey in medical image segmentation

This paper, published in 2021, received 397 indexed citations . Written by Jun Ma, Jianan Chen, Matthew Ng, Chen Li, Xiaoping Yang and Anne L. Martel covering the research area of Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (176 citations), Radiology, Nuclear Medicine and Imaging (167 citations), Artificial Intelligence (111 citations), Biomedical Engineering (68 citations) and Neurology (39 citations). Published in Medical Image Analysis.

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/10.1016/j.media.2021.102035.

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