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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Библиографические подробности
Главные авторы: Xiangmin Li, Jiamei Chen, Bo Luo, Minyan Xia, Xu Zhang, Hangjia Zhu, Yutian Zhang-Cai, Yongshun Chen, Yang Yang, Yaofeng Wen
Формат: Artigo
Язык:Inglês
Опубликовано: Elsevier 2025-05-01
Серии:Heliyon
Предметы:
Breast intraductal proliferative lesions
Breast cancer
Knowledge distillation
Convolutional neural network
Nuclei segmentation
Classification
Online-ссылка:http://www.sciencedirect.com/science/article/pii/S2405844025011375
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