Balanced-mixup for highly imbalanced medical image classification

Galdran, Adrian; Carneiro, Gustavo; González Ballester, Miguel Ángel, 1973-

doi:http://dx.doi.org/10.1007/978-3-030-87240-3_31

Balanced-mixup for highly imbalanced medical image classification

Citation

Galdran A, Carneiro G, González Ballester MA. Balanced-mixup for highly imbalanced medical image classification. In: de Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, Essert C, editors. Medical Image Computing and Computer Assisted Intervention (MICCAI 2021): 24th International Conference; 2021 Sep 27-Oct 01; Strasbourg, France. Cham: Springer; 2021. p. 323-33. DOI: 10.1007/978-3-030-87240-3_31

Abstract

Highly imbalanced datasets are ubiquitous in medical image classification problems. In such problems, it is often the case that rare classes associated to less prevalent diseases are severely under-represented in labeled databases, typically resulting in poor performance of machine learning algorithms due to overfitting in the learning process. In this paper, we propose a novel mechanism for sampling training data based on the popular MixUp regularization technique, which we refer to as Balanced-MixUp. In short, Balanced-MixUp simultaneously performs regular (i.e., instance-based) and balanced (i.e., class-based) sampling of the training data. The resulting two sets of samples are then mixed-up to create a more balanced training distribution from which a neural network can effectively learn without incurring in heavily under-fitting the minority classes. We experiment with a highly imbalanced dataset of retinal images (55K samples, 5 classes) and a long-tail dataset of gastro-intestinal video frames (10K images, 23 classes), using two CNNs of varying representation capabilities. Experimental results demonstrate that applying Balanced-MixUp outperforms other conventional sampling schemes and loss functions specifically designed to deal with imbalanced data. Code is released at https://github.com/agaldran/balanced_mixup

Description

Comunicació presentada a 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), celebrat del 27 de setembre a l'1 d'octubre de 2021 de manera virtual.