In the last decade, many digital slides have been available in the pathological field thanks to the spreading of new technologies for computerized acquisition. Often hardware and software tools and devices are different among biomedical analysis centers; consequently, the digital slides do not have the same representation using different colorization, exposition, contrast, brightness, and other distortions. Many computer vision algorithms are sensitive to these differences, and, in specific tasks such as image retrieval, color stain normalization can be a helpful technique to mitigate this misunderstanding. In this paper, we explored the effects of color stain normalization in the patches based on Hematoxylin and Eosin (H&E) image retrieval to measure how and how much it impacts the accuracy of this task providing an exhaustive analysis employing a standard dataset.

Effects of Color Stain Normalization in Histopathology Image Retrieval using Deep Learning / Rinaldi, A. M.; Russo, C.; Tommasino, C.. - (2022), pp. 26-33. (Intervento presentato al convegno 24th IEEE International Symposium on Multimedia, ISM 2022 tenutosi a ita nel 2022) [10.1109/ISM55400.2022.00010].

Effects of Color Stain Normalization in Histopathology Image Retrieval using Deep Learning

Rinaldi A. M.;Russo C.;Tommasino C.
2022

Abstract

In the last decade, many digital slides have been available in the pathological field thanks to the spreading of new technologies for computerized acquisition. Often hardware and software tools and devices are different among biomedical analysis centers; consequently, the digital slides do not have the same representation using different colorization, exposition, contrast, brightness, and other distortions. Many computer vision algorithms are sensitive to these differences, and, in specific tasks such as image retrieval, color stain normalization can be a helpful technique to mitigate this misunderstanding. In this paper, we explored the effects of color stain normalization in the patches based on Hematoxylin and Eosin (H&E) image retrieval to measure how and how much it impacts the accuracy of this task providing an exhaustive analysis employing a standard dataset.
2022
978-1-6654-7172-5
Effects of Color Stain Normalization in Histopathology Image Retrieval using Deep Learning / Rinaldi, A. M.; Russo, C.; Tommasino, C.. - (2022), pp. 26-33. (Intervento presentato al convegno 24th IEEE International Symposium on Multimedia, ISM 2022 tenutosi a ita nel 2022) [10.1109/ISM55400.2022.00010].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/915974
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact