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Comput Math Methods Med ; 2022: 1770531, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36238476

RESUMO

Results: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. Conclusion: Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients.


Assuntos
Neoplasias da Mama , Aprendizado Profundo , Algoritmos , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Feminino , Humanos , Processamento de Imagem Assistida por Computador/métodos , Antígeno Ki-67 , Imageamento por Ressonância Magnética/métodos
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