Evaluating Curriculum Learning Strategies for Pancreatic Cancer Prediction

Abstract

[Abstract]: In this work we applied Curriculum Learning (CL) to evaluate the performance of a machine learning (ML) model for pancreatic cancer prediction. As the dataset required it, we applied missing value imputation and data augmentation techniques. We compare different curriculum configurations in terms of pacing functions and we perform different experiments concluding that CL helps to train the ML model. Nevertheless, not all the configurations behave in the same way, and the best results were obtained when organising the curriculum in increasing levels of difficulty following exponential pacing.