Synthetic Data Generation for Morphological Analyses of Histopathology Images with Deep Learning Models

In this study, we introduce a new synthetic data generation procedure for augmentation of histopathology image data. This is an extension to our previous research in which we proved the possibility to apply deep learning models for morphological analysis of tumor cells, trained on synthetic data onl...

Full description

Saved in:
Bibliographic Details
Published in:Vietnam journal of computer science 2023-08, Vol.10 (3), p.373-389
Main Authors: Tabakov, Martin, Galus, Krzysztof, Zawisza, Artur, Chlopowiec, Adam R., Chlopowiec, Adrian B., Karanowski, Konrad
Format: Article
Language:English
Subjects:
data augmentation
Deep learning
histopathology images
Ki-67 proliferation index
synthetic data
tumor cells
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this study, we introduce a new synthetic data generation procedure for augmentation of histopathology image data. This is an extension to our previous research in which we proved the possibility to apply deep learning models for morphological analysis of tumor cells, trained on synthetic data only. The medical problem considered is related to the Ki-67 protein proliferation index calculation. We focused on the problem of cell counting in cell conglomerates, which are considered as structures composed of overlapping tumor cells. The lack of large and standardized data sets is a critical problem in medical image classification. Classical augmentation procedures are not sufficient. Therefore, in this research, we expanded our previous augmentation approach for histopathology images and we proved the possibility to apply it for a cell-counting problem.
ISSN:2196-8888
2196-8896
DOI:10.1142/S2196888823500057