A New Intra Fine-Tuning Method Between Histopathological Datasets in Deep Learning

Nassima Dif and Zakaria Elberrichi
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Nassima Dif: EEDIS Laboratory, Djillali Liabes University, Sidi Bel Abbes, Algeria
Zakaria Elberrichi: EEDIS Laboraory, Djillali Liabes University, Sidi Bel Abbes, Algeria

International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 2020, vol. 11, issue 2, 16-40

Abstract: This article presents a new fine-tuning framework for histopathological images analysis. Despite the most common solutions where the ImageNet models are reused for image classification, this research sets out to perform an intra-domain fine tuning between the trained models on the histopathological images. The purpose is to take advantage of the hypothesis on the efficiency of transfer learning between non-distant datasets and to examine for the first time these suggestions on the histopathological images. The Inception-v3 convolutional neural network architecture, six histopathological source datasets, and four target sets as base modules were used in this article. The obtained results reveal the importance of the pre-trained histopathological models compared to the ImageNet model. In particular, the ICIAR 2018-A presented a high-quality source model for the various target tasks due to its capacity in generalization. Finally, the comparative study with the other literature results shows that the proposed method achieved the best results on both CRC (95.28%) and KIMIA-PATH (98.18%) datasets.

Date: 2020
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