High-throughput adaptive sampling for whole-slide histopathology image analysis

A non-transitory computer-readable storage medium storing computer executable instructions that when executed by a computer control the computer to perform a method, the method comprising:

• accessing a radiological image of a region of tissue demonstrating invasive pathology, where the radiological image comprises a set of tiles;
• selecting a sample set of tiles from the set of tiles using a gradient-based quasi-Monte Carlo sampling approach;
• providing the sample set of tiles to an automated classifier;
• receiving, from the automated classifier, a classification of the sample set of tiles;
• generating an invasiveness probability map based, at least in part, on the classification;
• computing a gradient image based, at least in part, on the invasiveness probability map;
• and
• providing an invasiveness classification and localization based on the invasiveness probability map and the gradient image.

An apparatus for detecting cancerous pathology in a region of tissue, comprising:

• a processor;
• a memory;
• an input/output interface;
• a set of circuits;
• and
• an interface to connect the processor, the memory, the input/output interface and the set of circuits, where the set of circuits includes:
• an image acquisition circuit that acquires an image of a region of tissue demonstrating cancerous pathology, where the image comprises a set of tiles;
• an adaptive sampling circuit that selects a sampling subset of tiles from the image where the sampling subset of tiles comprises N tiles, where N is less that the number of tiles in the set of tiles, N being an integer;
• an invasiveness circuit that determines a probability of a presence of invasive pathology in a member of the sampling subset of tiles;
• a probability map circuit that generates an invasiveness probability map based, at least in part, on the probability;
• a probability gradient circuit that generates a gradient image based, at least in part, on the invasiveness probability map, where the gradient image identifies directional changes of the invasiveness probability map;
• and
• a classification circuit that classifies the region of tissue based, at least in part, on the probability map.