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S. Asgarian

1 paper hereh-index 5114 citations18 works total

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  • middle author1

Across the 1 of 1 paper where every author was matched, so the position is known.

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  • eess.IV1

identity via Semantic Scholar / OpenAlex

most citedComparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images

5 citations · 5 across the 1 of their papers we have counts for

collaborators

1 paper

eess.IV2023★ 5 cited

Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images

Mohsen Ahmadi, Masoumeh Farhadi Nia, Sara Asgarian +4

In this study, the main objective is to develop an algorithm capable of identifying and delineating tumor regions in breast ultrasound (BUS) and mammographic images. The technique…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.