Translational AI and Deep Learning in Diagnostic Pathology

Abstract

There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.

Keywords: artificial intelligence; computational pathology; deep learning; digital pathology; image analysis; machine learning; neural network; pathology.

Figure 1

U-Net architecture for semantic segmentation, comprising encoder (downsampling), and decoder (upsampling) sections, and showing the skip connections between layers (in yellow).

Figure 2

GANs, Generative adversarial networks (GANs) are deep neural network architectures comprised of two networks (generator and discriminator), opposing one against the other (thus the “adversarial”). The generator takes in random numbers and returns an image. This generated image is fed into the discriminator alongside a stream of images taken from the actual, ground-truth dataset.

Figure 3

Unsupervised Learning, unsupervised anomaly detection framework. Generative adversarial training is performed on healthy data and testing is performed on unseen data.

Figure 4

PD-L1 imaging in lung cancer. Deep learning can be used to identify and distinguish positive | negative tumor cells and positive | negative inflammatory cells.

Figure 5

Illustrates the current workflow in molecular research and diagnostics. Solid tumor analysis is commonly derived from FFPE block and H&E tissue section as guide for tumor content (far left). The figure shows the need for annotation and macrodissection and the importance of tumor purity from FFPE samples for molecular profiling. Digital pathology can automate the annotation and measurement of tumor cells in H&E—providing a more objective, reliable platform for molecular pathology.

Figure 6

Automated identification of colorectal tumor in H&E tissue samples using deep learning networks, showing heatmap of tumor regions (Left) and automatically generated macrodissection boundary (Right) with a product called TissueMark.

Figure 7

Automated analysis of cellular content in H&E using deep learning in TissueMark. Here tumor (red) and non-tumor cells (green) can be distinguished, annotated for visual inspection and counted to reach more precise qualititive measures of % tumor across entire whole slide H&E scans in lung, colon, melanoma, breast, and prostate tissue sections.