Authors:
Simon Gutwein
1
;
2
;
Martin Kampel
2
;
Sabine Taschner-Mandl
1
and
Roxane Licandro
3
Affiliations:
1
St. Anna Children’s Cancer Research Institute, Zimmermannplatz 10, Vienna, Austria
;
2
TU Wien, Faculty of Informatics, Institute of Visual Computing & Human-Centered Technology, Computer Vision Lab, Favoritenstr. 9/193-1, A-1040 Vienna, Austria
;
3
Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab (CIR), Waehringer Guertel 18-20, A-1090 Vienna, Austria
Keyword(s):
Genetic Alteration, Cancer Diagnostics, Two-Stream Network, Fluorescence in Situ Hybridization, Label Noise.
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 a
cts 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.
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