Generic Isolated Cell Image Generator.

ABSTRACT: Building automated cancer screening systems based on image analysis is currently a hot topic in computer vision and medical imaging community. One of the biggest challenges of such systems, especially those using state-of-the-art deep learning techniques, is that they usually require a large amount of training data to be accurate. However, in the medical field, the confidentiality of the data and the need for medical expertise to label them significantly reduce the amount of training data available. A common practice to overcome this problem is to apply data set augmentation techniques to artificially increase the size of the training data set. Classical data set augmentation methods such as geometrical or color transformations are efficient but still produce a limited amount of new data.

SUBMITTER: Scalbert M 

PROVIDER: S-EPMC6899488 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature