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US Patent 10049450 High-throughput adaptive sampling for whole-slide histopathology image analysis

Patent 10049450 was granted and assigned to Case Western Reserve University on August, 2018 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
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Patent attributes

Patent Applicant
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Current Assignee
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
100494501
Patent Inventor Names
Anant Madabhushi1
Angel Alfonso Cruz Roa1
Fabio Gonzalez1
Date of Patent
August 14, 2018
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Patent Application Number
152822331
Date Filed
September 30, 2016
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Patent Citations Received
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2
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US Patent 11348661 Predicting total nucleic acid yield and dissection boundaries for histology slides
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US Patent 11348240 Predicting total nucleic acid yield and dissection boundaries for histology slides
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US Patent 11348239 Predicting total nucleic acid yield and dissection boundaries for histology slides
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US Patent 11449998 Processing of histology images with a convolutional neural network to identify tumors
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US Patent 11868440 Statistical model training systems
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US Patent 11935152 Determining biomarkers from histopathology slide images
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US Patent 10282589 Method and system for detection and classification of cells using convolutional neural networks
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Patent Primary Examiner
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Soo Park
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Patent 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.

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