Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KDCNN-NSAC)

Background and objective: Diagnosis of breast intraductal proliferative lesions (BIDPLs) in hematoxylin-eosin (HE) images remains a time-consuming and intractable topic because of subjective processes and subtle morphological differences. Convolutional neural networks (CNNs) show great potential for...

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Autores principales:	Xiangmin Li, Jiamei Chen, Bo Luo, Minyan Xia, Xu Zhang, Hangjia Zhu, Yutian Zhang-Cai, Yongshun Chen, Yang Yang, Yaofeng Wen
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Elsevier 2025-05-01
Colección:	Heliyon
Materias:	Breast intraductal proliferative lesions Breast cancer Knowledge distillation Convolutional neural network Nuclei segmentation Classification
Acceso en línea:	http://www.sciencedirect.com/science/article/pii/S2405844025011375
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