Transfer learning from synthetic labels for histopathological images classification

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 52; no. 1; pp. 358 - 377
• Main Authors: Dif, Nassima, Attaoui, Mohammed Oualid, Elberrichi, Zakaria, Lebbah, Mustapha, Azzag, Hanene
• Format: Journal Article
• Language: English
• Published: New York Springer US 2022; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Artificial Intelligence; Clustering; Comparative studies; Computer Science; Datasets; Image classification; Labels; Machines; Manufacturing; Mechanical Engineering; Processes; Unsupervised learning; Clustering; Convolutional neural networks; Transfer learning; Histopathological datasets
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-021-02425-z

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.