Active Learning for Breast Cancer Identification
This work addresses the labor-intensive and variable nature of manual breast cancer diagnosis for medical professionals, but it is incremental as it builds on existing deep learning methods with a specific training enhancement.
The paper tackles the problem of automating breast cancer identification from images by proposing a reversed active learning (RAL) strategy to remove mislabeled training data, which improves the accuracy of a CNN from 93.75% to 96.25% on a public dataset.
Breast cancer is the second most common malignancy among women and has become a major public health problem in current society. Traditional breast cancer identification requires experienced pathologists to carefully read the breast slice, which is laborious and suffers from inter-observer variations. Consequently, an automatic classification framework for breast cancer identification is worthwhile to develop. Recent years witnessed the development of deep learning technique. Increasing number of medical applications start to use deep learning to improve diagnosis accuracy. In this paper, we proposed a novel training strategy, namely reversed active learning (RAL), to train network to automatically classify breast cancer images. Our RAL is applied to the training set of a simple convolutional neural network (CNN) to remove mislabeled images. We evaluate the CNN trained with RAL on publicly available ICIAR 2018 Breast Cancer Dataset (IBCD). The experimental results show that our RAL increases the slice-based accuracy of CNN from 93.75% to 96.25%.