The Mammographic Image Analysis Society digital mammogram database

848 indexed citations

Abstract

loading...

About

This paper, published in 1994, received 848 indexed citations. Written by John Suckling, James Parker, Susan Astley, I. Hutt, Caroline Boggis, Ian W. Ricketts, Emmanuel A. Stamatakis, PM Taylor and Dibendu Betal covering the research area of Pulmonary and Respiratory Medicine, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. It is primarily cited by scholars working on Artificial Intelligence (722 citations), Computer Vision and Pattern Recognition (540 citations) and Radiology, Nuclear Medicine and Imaging (315 citations). Published in .

In The Last Decade

doi.org/w53799101 →

Countries where authors are citing The Mammographic Image Analysis Society digital mammogram database

Specialization
Citations

This map shows the geographic impact of The Mammographic Image Analysis Society digital mammogram database. 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 The Mammographic Image Analysis Society digital mammogram database with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites The Mammographic Image Analysis Society digital mammogram database more than expected).

Fields of papers citing The Mammographic Image Analysis Society digital mammogram database

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of The Mammographic Image Analysis Society digital mammogram database. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the The Mammographic Image Analysis Society digital mammogram database.

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/w53799101.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026