A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets

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This paper, published in 1950, received 294 indexed citations. Written by Hui Li, Benjamin Q. Huynh and Maryellen L. Giger covering the research area of Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. It is primarily cited by scholars working on Radiology, Nuclear Medicine and Imaging (241 citations), Artificial Intelligence (208 citations) and Biomedical Engineering (45 citations). Published in Medical Physics.

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This map shows the geographic impact of A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. 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 A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets more than expected).

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This network shows the impact of A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets.

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This paper is also available at doi.org/10.1002/mp.12453.

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