Intermediate Task Fine-Tuning in Cancer Classification




deep learning, digital pathology, histopathology, intermediate task fine-tuning, transfer learning


Reducing the amount of annotated data required to train predictive models is one of the main challenges in applying artificial intelligence to histopathology.
In this paper, we propose a method to enhance the performance of deep learning models trained with limited data in the field of digital pathology. The method relies on a two-stage transfer learning process, where an intermediate model serves as a bridge between a pre-trained model on ImageNet and the final cancer classification model. The intermediate model is fine-tuned with a dataset of over 4,000,000 images weakly labeled with clinical data extracted from TCGA program. The model obtained through the proposed method significantly outperforms a model trained with a traditional transfer learning process.


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How to Cite

García, M. A., Gramática, M. N., & Ricapito, J. P. (2023). Intermediate Task Fine-Tuning in Cancer Classification. Journal of Computer Science and Technology, 23(2), e12.



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