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Transfer learning from synthetic labels for histopathological images classification

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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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Notes

  1. The main difference between the VGGNet and the InceptionV3 architectures is the nature of convolutional layers. VGGNet is based on the standard convolutional layers. In contrast, InceptionV3 exploit Inception blocks, where each Inception module regroups many convolutions.

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Acknowledgements

We address with a special thanks, Mm. Rezki Hanene, pathologist at the Hospital of Sidi Bel Abbes Algeria, for giving us medical significance to our results. We thank her for her availability and the time she gave to this work.

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Authors and Affiliations

  1. EEDIS Laboratory, Djillali Liabes University, Sidi Bel Abbes, Algeria

    Nassima Dif & Zakaria Elberrichi

  2. University Sorbonne Paris Nord, LIPN-UMR 7030 99, av. J-B Clément, 93430, Villetaneuse, France

    Mohammed Oualid Attaoui, Mustapha Lebbah & Hanene Azzag

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  1. Nassima Dif
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  2. Mohammed Oualid Attaoui
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Correspondence to Nassima Dif.

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Dif, N., Attaoui, M.O., Elberrichi, Z. et al. Transfer learning from synthetic labels for histopathological images classification. Appl Intell 52, 358–377 (2022). https://doi.org/10.1007/s10489-021-02425-z

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