GENUINE: Genomic and Nucleus Information Embedding for Single Cell Genetic Alteration Classification in Microscopic Images

Simon Gutwein, Simon Gutwein, Martin Kampel, Sabine Taschner-Mandl, Roxane Licandro

2024

Abstract

Fluorescence in situ hybridization (FISH) is an essential technique in cancer diagnostics, providing valuable insights into the genetic aberrations typical of malignancies. However, the effectiveness of FISH analysis is often impeded by the susceptibility of conventional classification algorithms to variations in image appearances, coupled with a reliance on manually crafted decision rule design, limiting their adaptability and precision. To address these challenges, we introduce GENUINE, an innovative two-stream network that combines whole image information through a convolutional neural network encoder and incorporates a single FISH signal stream dedicated to the analysis of individual signals. Our results demonstrate that GENUINE achieves remarkable accuracy not only on datasets resembling the training data distributions, but also on previously unseen data, underscoring its robustness and generalizability. Moreover, we present evidence that the architecture of GENUINE inherently acts as a regularizer during training against label noise. This leads to the extraction of meaningful features and thereby fosters a biological relevant organization of the feature space. The development of GENUINE marks a significant advancement in the utilization of FISH for cancer diagnostics, providing a robust and versatile tool capable of navigating the complexities of genetic aberrations in malignancies.

Paper Citation

in Harvard Style

Gutwein S., Kampel M., Taschner-Mandl S. and Licandro R. (2024). GENUINE: Genomic and Nucleus Information Embedding for Single Cell Genetic Alteration Classification in Microscopic Images. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 27-36. DOI: 10.5220/0012319700003654

in Bibtex Style

@conference{icpram24,
author={Simon Gutwein and Martin Kampel and Sabine Taschner-Mandl and Roxane Licandro},
title={GENUINE: Genomic and Nucleus Information Embedding for Single Cell Genetic Alteration Classification in Microscopic Images},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={27-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012319700003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - GENUINE: Genomic and Nucleus Information Embedding for Single Cell Genetic Alteration Classification in Microscopic Images
SN - 978-989-758-684-2
AU - Gutwein S.
AU - Kampel M.
AU - Taschner-Mandl S.
AU - Licandro R.
PY - 2024
SP - 27
EP - 36
DO - 10.5220/0012319700003654
PB - SciTePress