Robust Influence-based Training Methods for Noisy Brain MRI
Minh-Hao Van Alycia N. Carey Xintao Wu
Correctly classifying brain tumors is imperative to the prompt and accurate
treatment of a patient. While several classification algorithms based on
classical image processing or deep learning methods have been proposed to
rapidly classify tumors in MR images, most assume the unrealistic setting of
noise-free training data. In this work, we study a difficult but realistic
setting of training a deep learning model on noisy MR images to classify brain
tumors. We propose two training methods that are robust to noisy MRI training
data, Influence-based Sample Reweighing (ISR) and Influence-based Sample
Perturbation (ISP), which are based on influence functions from robust
statistics. Using the influence functions, in ISR, we adaptively reweigh
training examples according to how helpful/harmful they are to the training
process, while in ISP, we craft and inject helpful perturbation proportional to
the influence score. Both ISR and ISP harden the classification model against
noisy training data without significantly affecting the generalization ability
of the model on test data. We conduct empirical evaluations over a common brain
tumor dataset and compare ISR and ISP to three baselines. Our empirical results
show that ISR and ISP can efficiently train deep learning models robust against
noisy training data.