Table 2.

Deep learning concepts in GUP.

Algorithm training	During training, an algorithmic model uses labeled data tagged with designating properties, characteristics, and classifications describing an object of interest, such as WSI, to learn - thereby increasing the model’s propensity for correct predictive diagnostics, among other decision-making processes. Algorithms are classified as “supervised” (requiring human intervention), or unsupervised. Supervised machine learning techniques are utilized in numerous medical specialties, with recent application in pathology. During training, an algorithmic model uses labeled data tagged with designating properties, characteristics, and classifications describing an object of interest, such as WSI, to learn - thereby increasing the model’s propensity for correct predictive diagnostics, among other decision-making processes.
Artificial neural network (ANN)	Deep learning techniques are a subset of machine-learning algorithms which use artificial neural networks (ANN) to develop independent interpretative or predictive ability from unstructured or unlabeled data. With no predefined output set, the algorithm is enabled to determine what it defines as natural patterns present in the GUP input (WSIs). Such techniques use multiple layers of artificial neural networks to extract higher-level input features.
Convolutional neural network (CNN)	CNNs used in WSI analysis share the fundamental architecture of deep neural networks (input layer, multiple hidden layers, output layer). Each CNN layer responds to different pattern-forming features within a WSI. Features such as object boundaries can be elicited during automatic segmentation through CNN, i.e., “feature extraction”. Manual annotation through digital segmentation tools is also performed by GUP specialists for defining “ground-truth” classifications for object boundaries to benchmark neural network performance.
Area-based measurements	Quantify the parameters of basic elements forming the blueprint of a WSI. Pixel-based assessment is applied to area-based measurements wherein the color or intensity of staining in each pixel within a designated area is quantified through algorithm(s).
Cell-based measurements	Cell-based measurements identify and enumerate cells or nuclei through morphometry-based assessment, through which similar pixels (e.g., in size or shape) are grouped to predefined cell-structure profiles meeting specific criteria.