Improving Uncertainty Estimations for Mammogram Classification using Semi-Supervised Learning
Saul Calderon-Ramirez, Diego Murillo-Hernandez, Kevin Rojas-Salazar, Luis-Alexander Calvo-Valverd, Shengxiang Yang, Armaghan Moemeni, David Elizondo, Ezequiel López‐Rubio, Miguel A. Molina‐Cabello
Abstract
Computer aided diagnosis for mammogram images have seen positive results through the usage of deep learning architectures. However, limited sample sizes for the target datasets might prevent the usage of a deep learning model under real world scenarios. The usage of unlabeled data to improve the accuracy of the model can be an approach to tackle the lack of target data. Moreover, important model attributes for the medical domain as model uncertainty might be improved through the usage of unlabeled data. Therefore, in this work we explore the impact of using unlabeled data through the implementation of a recent approach known as MixMatch, for mammogram images. We evaluate the improvement on accuracy and uncertainty of the model using popular and simple approaches to estimate uncertainty. For this aim, we propose the usage of the uncertainty balanced accuracy metric.