Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2023 Sep 14:14:100335.
doi: 10.1016/j.jpi.2023.100335. eCollection 2023.

Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey

Khaled Al-Thelaya  1 , Nauman Ullah Gilal  1 , Mahmood Alzubaidi  1 , Fahad Majeed  1 , Marco Agus  1 , Jens Schneider  1 , Mowafa Househ  1
Affiliations
Review

Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey

Khaled Al-Thelaya et al. J Pathol Inform. .

Abstract

Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by "engineered" methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology.

Keywords: Deep learning; Digital pathology applications; Image features engineering; Whole slide images.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Typical workflow of WSI processing using a simple CNN architecture.
Fig. 2
Fig. 2
Typical workflow of WSI graph deep embedding using GCN.
Fig. 3
Fig. 3
Typical workflow of WSI segmentation applications.
Fig. 4
Fig. 4
Typical workflow of WSI images retrieval applications.
Fig. 5
Fig. 5
Typical workflow of WSI visualization applications.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Abels E., Pantanowitz L., Aeffner F., et al. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the digital pathology association. J Pathol. 2019;249:286–294. doi: 10.1002/path.5331. - DOI - PMC - PubMed
    1. Achanta R., Shaji A., Smith K., Lucchi A., Fua P., Süsstrunk S. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell. 2012;34:2274–2282. doi: 10.1109/TPAMI.2012.120. - DOI - PubMed
    1. Adiga U., Malladi R., Fernandez-Gonzalez R., de Solorzano C. High-throughput analysis of multispectral images of breast cancer tissue. IEEE Trans Image Process. 2006;15:2259–2268. doi: 10.1109/TIP.2006.875205. - DOI - PubMed
    1. Agus M., Al-Thelaya K., Calí C., et al. Eurographics Workshop on Visual Computing for Biology and Medicine. The Eurographics Association; 2020. InShaDe: invariant shape descriptors for visual analysis of histology 2D cellular and nuclear shapes; pp. 61–70. - DOI
    1. Ahmedt-Aristizabal D., Armin M.A., Denman S., Fookes C., Petersson L. A survey on graph-based deep learning for computational histopathology. Comput Med Imaging Graphics. 2021:102027. doi: 10.1016/j.compmedimag.2021.102027. https://www.sciencedirect.com/science/article/pii/S0895611121001762 - DOI - PubMed

LinkOut - more resources