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Cell image augmentation for classification task using GANs on Pap smear dataset

Zak Jakub, Grzeszczyk Michal K., Pater Antonina, Roszkowiak Lukasz, Siemion Krzysztof, Korzynska Anna

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 3

995--1011

One of the solutions to the problem of insufficiently large training datasets in image processing is data augmentation. This process artificially extends the size of training datasets to avoid overfitting. Generative Adversarial Networks yield that become increasingly difficult to differentiate from real images, until the differentiation is no longer possible. Thus, artificial images closely resembling original ones can be generated. Inclusion of artificial images contributes to improving the training process. Medical domain is one of the areas where data acquisition is burdened by many procedures, laws, and prohibitions. As a result the potential size of collected datasets is reduced. This article presents the results of training Convolutional Neural Networks on a artificially extended image datasets. The resulting classification accuracy on a cell classification task of models trained with images generated using the proposed method were increased by up to 12.9% in comparison to that of the model trained only with original dataset from the HErlev Pap smear dataset.

A review of current systems for annotation of cell and tissue images in digital pathology

Korzynska Anna, Roszkowiak Lukasz, Zak Jakub, Siemion Krzysztof

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 4

1436--1453

With the advent and great advances of methods based on deep learning in image analysis, it appears that they can be effective in digital pathology to support the work of pathologists. However, a major limitation in the development of computer-aided diagnostic systems for pathology is the cost of data annotation. Evaluation of tissue (histopathological) and cellular (cytological) specimens seems to be a complex challenge. To simplify the laborious process of obtaining a sufficiently large set of data, a number of different systems could be used for image annotation. Some of these systems are reviewed in this paper with a comparison of their capabilities.