A method for balancing a multi-labeled biomedical dataset

Abstract

In this paper, we propose a data balancing method for multi-label biomedical data. The method can be applied in the case of semantic segmentation problems for balancing the corresponding image data. The proposed method performs oversampling of instances of minority classes in a way that increases the frequencies of appearance (a ratio of number of samples, containing this class, over the total number of samples in the dataset) of minority classes in the data, thereby reducing the class imbalance. The effectiveness of the proposed method is shown experimentally by applying it to two highly unbalanced biomedical image datasets. A convolutional neural network (CNN) was trained on several versions of those datasets: one balanced with the proposed method, another balanced with manual oversampling and an unbalanced version. The results of the experiments validate the effectiveness of the proposed method, proving that it allows the influence of class imbalance on the learning algorithm to be reduced, thus improving its original classification results for most of the classes. Apart from biomedical image data, the proposed method was applied to several common multi-label datasets. Inherently, the proposed method does not make any assumptions about the underlying structure of the data to be balanced; therefore, it can be applied to all types of data (vectors, images, etc.) that can be described in a multi-label framework. It also can be used in conjunction with any learning algorithm that is suitable for multi-label data. To illustrate its wider applicability, a series of experiments was conducted using seven common multi-label datasets. An experimental comparison to existing multi-label data balancing approaches is provided, as well. The experimental results show that the proposed method presents a competitive alternative to existing approaches.