Application of deep learning to predict underestimation in ductal carcinoma in situ of the breast with ultrasound.

Qian, Lang; Lv, Zhikun; Zhang, Kai; Wang, Kun; Zhu, Qian; Zhou, Shichong; Chang, Cai; Tian, Jie

Qian, Lang; Lv, Zhikun; Zhang, Kai; Wang, Kun; Zhu, Qian; Zhou, Shichong; Chang, Cai; Tian, Jie.

Afiliación

Qian L; Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.
Lv Z; Department of Oncology, Fudan University, Shanghai Medical College, Shanghai, China.
Zhang K; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Wang K; School of Artiï¬cial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Zhu Q; Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.
Zhou S; Department of Oncology, Fudan University, Shanghai Medical College, Shanghai, China.
Chang C; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Tian J; School of Artiï¬cial Intelligence, University of Chinese Academy of Sciences, Beijing, China.

Ann Transl Med ; 9(4): 295, 2021 Feb.

Article en En | MEDLINE | ID: mdl-33708922

Palabras clave

Artificial intelligence (AI); core needle biopsy (CNB); ductal carcinoma in situ (DCIS); prediction of upstaging

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Ann Transl Med Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: China

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