A Data Augmentation Methodology to Reduce the Class Imbalance in Histopathology Images.

ABSTRACT: Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and multi-label classification is class imbalance, where one or several classes make up a substantial portion of the total instances. This imbalance causes the neural network to prioritize features of the majority classes during training, as their detection leads to higher scores. In the context of object detection, two types of imbalance can be identified: (1) an imbalance between the space occupied by the foreground and background and (2) an imbalance in the number of instances for each class. This paper aims to address the second type of imbalance

SUBMITTER: Escobar Diaz Guerrero R 

PROVIDER: S-EPMC11300732 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature