Transfer learning from synthetic labels for histopathological images classification
Transfer learning from synthetic labels for histopathological images classification
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DOI：
10.1007/s10489-021-02425-z
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发表时间：
2021-04-29
影响因子：
5.3
通讯作者：
Azzag, Hanene
中科院分区：
文献类型：
--
作者：
Dif, Nassima;Attaoui, Mohammed Oualid;Azzag, Hanene
这项研究介绍了一种新的策略，结合了无监督学习（聚类）和迁移学习。采用聚类方法为源数据集生成合成标签（ICAR-2018）。然后将生成的数据集用于将学习转移到其他组织病理学数据集（KimiaPath 960，CRC，Biomaging- 2015，Breakhis和Lymphoma）。基于两种聚类算法（K-means和多目标聚类流）的比较研究表明了MOC-Stream的有效性。通过该聚类算法生成的合成组织病理学数据集在迁移学习中优于原始标记数据集和imageNet模型。
This study introduces a new strategy that combines unsupervised learning (clustering) and transfer learning. Clustering methods are employed to generate synthetic labels for the source dataset (ICAR-2018). The generated dataset is then used for transfer learning to other histopathological datasets (KimiaPath960, CRC, Biomaging- 2015, Breakhis, and Lymphoma). The comparative study based on two clustering algorithms (K-means and multi-objective clustering stream) demonstrates the efficiency of MOC-Stream. The generated synthetic histopathological dataset by this clustering algorithm outperformed the original labeled dataset and the imageNet models in transfer learning.