The benefits of digital pathology are manifold, the following graph summarizes these for the general pathology lab. These benefits are applicable to many areas and industries including academic research, preclinical labs of pharmaceutical companies and CROs, as well as the diagnostic pathology laboratory.
Deep learning (DL) is a subset of machine learning (ML) which falls under the category of artificial intelligence (AI). The formation of layers is not only pervasive in the nomenclature of these different types of computer intelligence but also in their architecture. DL is based on artificial neural networks, a layered and connected system of algorithms receiving and processing information.
Artificial neural networks (ANNs) are what form and drive deep learning. They are computing systems designed to find patterns that are too complex to be manually taught to machines to recognize. Hence why deep learning is so adept at image analysis and in some regards more powerful than machine learning.
ANNs are made up of a collection of connected units of mathematical functions, referred to as artificial neurons. In deep learning models the neurons can range in amount from dozens to millions of units always arranged in a series of layers. The neurons are connected to each other between the different layers with a series of connectors, called weighted connections.
A very simplified explanation of how the layers work together when processing information through deep learning is broken down here:
The information being processed in neural networks can travel through the layers, neurons, and connections in different ways: only in one direction from input to output, or in multiple directions, and more permutations of these. These ways differ between different types of ANNs. We will now dive into the type of neural network deployed by Aiforia: convolutional neural networks.
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Pantanowitz L. Digital Pathology. In: Pantanowitz L, Parwani AV, editors. Chicago, USA: ASCP Press;
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6289005/
https://www.rcpath.org/profession/digital-pathology.html