Abstract: Methods, apparatus, and other embodiments associated with classifying a region of tissue represented in a digitized whole slide image (WSI) using iterative gradient-based quasi-Monte Carlo (QMC) sampling. One example apparatus includes an image acquisition circuit that acquires a WSI of a region of tissue demonstrating cancerous pathology, an adaptive sampling circuit that selects a subset of tiles from the WSI using an iterative QMC Sobol sequence sampling approach, an invasiveness circuit that determines a probability of a presence of invasive pathology in a member of the subset of tiles, a probability map circuit that generates an invasiveness probability map based on the probability, a probability gradient circuit that generates a gradient image based on the invasiveness probability map, and a classification circuit that classifies the region of tissue based on the probability map. A prognosis or treatment plan may be provided based on the classification of the WSI.

Abstract: Methods, apparatus, and other embodiments associated with classifying a region of tissue represented in a digitized whole slide image (WSI) using iterative gradient-based quasi-Monte Carlo (QMC) sampling. One example apparatus includes an image acquisition circuit that acquires a WSI of a region of tissue demonstrating cancerous pathology, an adaptive sampling circuit that selects a subset of tiles from the WSI using an iterative QMC Sobol sequence sampling approach, an invasiveness circuit that determines a probability of a presence of invasive pathology in a member of the subset of tiles, a probability map circuit that generates an invasiveness probability map based on the probability, a probability gradient circuit that generates a gradient image based on the invasiveness probability map, and a classification circuit that classifies the region of tissue based on the probability map. A prognosis or treatment plan may be provided based on the classification of the WSI.

Abstract: Methods, apparatus, and other embodiments associated with classifying a region of tissue represented in a digitized whole slide image (WSI) using iterative gradient-based quasi-Monte Carlo (QMC) sampling. One example apparatus includes an image acquisition circuit that acquires a WSI of a region of tissue demonstrating cancerous pathology, an adaptive sampling circuit that selects a subset of tiles from the WSI using an iterative QMC Sobol sequence sampling approach, an invasiveness circuit that determines a probability of a presence of invasive pathology in a member of the subset of tiles, a probability map circuit that generates an invasiveness probability map based on the probability, a probability gradient circuit that generates a gradient image based on the invasiveness probability map, and a classification circuit that classifies the region of tissue based on the probability map. A prognosis or treatment plan may be provided based on the classification of the WSI.

Abstract: Methods, apparatus, and other embodiments associated with classifying a region of tissue represented in a digitized whole slide image (WSI) using iterative gradient-based quasi-Monte Carlo (QMC) sampling. One example apparatus includes an image acquisition circuit that acquires a WSI of a region of tissue demonstrating cancerous pathology, an adaptive sampling circuit that selects a subset of tiles from the WSI using an iterative QMC Sobol sequence sampling approach, an invasiveness circuit that determines a probability of a presence of invasive pathology in a member of the subset of tiles, a probability map circuit that generates an invasiveness probability map based on the probability, a probability gradient circuit that generates a gradient image based on the invasiveness probability map, and a classification circuit that classifies the region of tissue based on the probability map. A prognosis or treatment plan may be provided based on the classification of the WSI.