Intermediate Task Fine-Tuning in Cancer Classification Clasificación de Cancer mediante Transferencia de Conocimiento con Tarea Intermedia

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 pretrained 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.

Automated summary

This paper proposes a method to enhance the performance of deep learning models trained with limited data in the field of digital pathology, using a two-stage transfer learning process.