Medical Predictive Modelling using Transfer Learning

• Author / Creator

  Vaid, Samridhi

• Deep learning has had much success on challenging problems with large datasets. However, it struggles in cases with limited training data. Transfer learning represents a class of approaches for transferring knowledge from large source datasets to smaller target datasets. But many transfer learning approaches have constraints in terms of dataset size and similarity of output features. In this thesis, we introduce Quality-Diversity Transfer Learning (QDTL), a novel transfer learning approach based on neuroevolution for dealing with very small dataset problems with distinct output features. We demonstrate the success of QDTL on two medical prediction problems, outperforming standard transfer learning baselines.

• Subjects / Keywords

  • Transfer Learning
  • Medical Prediction
  • Quality Diversity

• Graduation date

  Fall 2023

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-zt95-rf53

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.