Histopathological Image Analysis: A Review

By Metin Nafi Gurcan, L. Boucheron, A. Can, Anant Madabhushi, Nasir M. Rajpoot, Bülent Yener. IEEE Reviews in Biomedical Engineering, 2009

1,982 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/bd898f483476e3dcacf83cd85efc64e6319da0e1

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Citations in the corpus

Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features (2017, background): “Histopathology image analysis is a gold standard for cancer recognition and diagnosis [1, 2].”
A review of the application of deep learning in medical image classification and segmentation (2020, background): “The contradiction between a large number of market demands and a shortage of talents in the field of pathology gives a great opportunity for deep learning in this field, and digital pathology technology also makes computer-assisted diagnosis possible (41).”
Stain Specific Standardization of Whole-Slide Histopathological Images (2016, neutral): “Pathologists make diagnosis of the disease based on features such as morphology and spatial arrangement of cells [2].”
Mitosis detection in breast cancer histological images An ICPR 2012 contest (2013, background): “However, consistency, reproducibility and agreement on mitotic count for the same slide can vary largely among pathologists.[2,3] An automatic tool for this task may help for reaching a better consistency, and at the same time reducing the burden of this demanding task for pathologists.”
Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks (2020, background): “Previously due to the lack of computational power, most of the literature focused on regions of interest (ROI) patches which are selected by pathologists from WSIs [9].”
Deep learning for magnification independent breast cancer histopathology image classification (2016, methodology): “For more information on automated image analysis methods in histology, we refer reader to [3], [4], and [6].”
Histology image analysis for carcinoma detection and grading (2012, methodology): “For histology image analysis, besides a general description of current CAD functional modules as in the recent survey [60], we also provide a detailed, though introductory, example of the image segmentation module, by illustrating the performance of our methods and nine commonly used approaches.”
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network (2018, background): “ly stained with Hematoxylin and Eosin (H&E) prior to the visual analysis performed by the specialist. During the diagnosis process, the affected region is determined from whole-slide tissue scans [3]. In addition, the pathologist analyzes microscopic images of the tissue samples from the biopsy ”
Review of spectral imaging technology in biomedical engineering: achievements and challenges (2013, background): “This may be due to the fact that spectral imaging does not work ideally with transparent chromogens, and the number of stains present in the sample as well as the characteristics of the stains themselves will affect the performance of spectral imaging analysis methods.(247,248) With the improvement ”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, methodology): “Another global color normalization technique is known as histogram specification [29,48].”
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images (2018, background): “The shape and distribution of cell nuclei in pathology images is used to determine cell, tissue, and cancer types, and is critical in cancer identification, grading, and prognosis [10].”
An Automatic Learning-Based Framework for Robust Nucleus Segmentation (2016, background): “Recent literature [1], [2] shows that computer-aid image analysis can significantly improve the objectivity and reproducibility.”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, positive): “…as individual images can be several gigabytes large; extracting patches involves many design choices; the classes are typically very unbalanced; and evaluation often relies on more complex slide-level measures such as the free-response receiver operating characteristic (FROC) (Gurcan et al., 2009).”