Transfer learning from synthetic labels for histopathological images classification |
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Authors: | Dif Nassima Attaoui Mohammed Oualid Elberrichi Zakaria Lebbah Mustapha Azzag Hanene |
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Affiliation: | 1.EEDIS Laboratory, Djillali Liabes University, Sidi Bel Abbes, Algeria ;2.University Sorbonne Paris Nord, LIPN-UMR 7030 99, av. J-B Clément, 93430, Villetaneuse, France ; |
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Abstract: | 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. |
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