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When AI Meets Digital Pathology

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Women in Computational Intelligence

Part of the book series: Women in Engineering and Science ((WES))

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Abstract

Pathology diagnosis relies mainly on the pathologists performing the examination on specimen glasses through microscopic. The process is time-consuming and highly experience-dependent. Recent advance in scanning speed, scanning resolution, and image quality shunts the lights on digitizing pathology images. Many promising and successful stories of artificial intelligence (AI) in diagnosing assistance further boost the acceptance of digital pathology. It is anticipated that with pathology slides digitized, tremendous benefits from AI assistive analysis can be brought in. While the lights have been shone, several issues also arise with the processing of digitized pathology images. The digitized pathology image has an image size of gigabytes. The targets for analysis could be very small and distributed all over the whole slide of images or mixed with various nontarget tissues. Some targets could appear of very large or very small sizes. These pose challenges on the labelling and processing of the whole slide pathology images. This chapter presents the issues and methods which have been developed to address the issues, when AI has been brought in as the assisted tools for digital pathology. Several examples on liver pathology image analysis are also presented.

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Abbreviations

WSI:

whole slide image

CAD:

computer-aided detection and/or diagnosis

AI:

artificial intelligence

CT:

computed tomography

DCNN:

deep convolutional neural network

CE:

Communate Europpene

FAD:

US food and drug administration

CNN:

convolutional neural network

H&E:

haematoxylin and eosin

IHC:

immunohistochemistry

TLI:

tumor lymphocytic infiltration

SPM:

spatial pyramid matching framework

SVM:

support vector machine

DCAN:

deep contour-aware network

ASPP:

atrous spatial pyramid pooling

MSCN:

multiscale convolutional network

FA-MSCN:

Feature-Aligned Multiscale Convolutional Network

SSCN:

single scale convolutional network

IoU:

intersection over Union

CAM:

class activation mapping

AIH:

autoimmune hepatitis

kNN:

k-nearest neighbors

SMOTE:

synthetic minority oversampling technique

ConvNet:

convolutional network

AL:

active learning

HoG:

Histogram of Oriented Gradients

SIFT:

scale-invariant feature transform

FCN:

fully convolution network

MC:

Monte Carlo

R-CNN:

Region-based CNN

IEAL:

imbalance effective active learning

DL:

dice loss

DICOM:

Digital Imaging and Communications in Medicine

IHE:

Integrating the Healthcare Enterprise

ICC:

International Color Consortium

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Acknowledgments

The authors would like to thank Drs. Hung-Wen Tsai and Nan-Haw Chow of National Cheng Kung University Hospital and Tsung-Lung Yang of Kaohsiung Veteran General Hospital for their data support and provision of domain knowledge. The authors would also like to thank Wei-Che Huang and Qi-En Xiao of National Cheng Kung University for making their experimental data available. The authors would like to thank National Center for High Performance Computing, Taiwan, for providing the computing power. Finally, the authors wish to acknowledge the financial support provided to this work by the Ministry of Science and Technology (MOST), Taiwan, under Grant No. MOST 108-2634-F-006 -004.

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Authors and Affiliations

  1. National Cheng Kung University, Tainan, Taiwan

    Pau-Choo Julia Chung & Chao-Ting Li

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  1. Pau-Choo Julia Chung
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Correspondence to Pau-Choo Julia Chung .

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Editors and Affiliations

  1. Department of Industrial and Systems Engineering and Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA

    Alice E Smith

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Julia Chung, PC., Li, CT. (2022). When AI Meets Digital Pathology. In: Smith, A.E. (eds) Women in Computational Intelligence. Women in Engineering and Science. Springer, Cham. https://doi.org/10.1007/978-3-030-79092-9_6

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