Histomorphometric classifier to predict cardiac failure from whole-slide hematoxylin and eosin stained images

US10528848B2 · kind B2 · utility

Assignee

• CASE WESTERN RESERVE UNIVERSITY · US

Inventors

• Anant Madabhushi · Beachwood, US
• Jeffrey John Nirschl · Philadelphia, US
• Andrew Janowczyk · East Meadow, US
• Eliot G. Peyster · Philadelphia, US
• Michael D. Feldman · Philadelphia, US
• Kenneth B. Margulies · Villanova, US

Key dates

• Filing date: Oct 31, 2017
• Grant date: Jan 7, 2020
• Expiry date: Jul 7, 2038

Classification

• Technology area (CPC G): Physics
• CPC primary: G06V2201/03
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Methods, apparatus, and other embodiments predict heart failure from WSIs of cardiac histopathology using a deep learning convolutional neural network (CNN). One example apparatus includes a pre-processing circuit configured to generate a pre-processed WSI by downsampling a digital WSI; an image acquisition circuit configured to randomly select a set of non-overlapping ROIs from the pre-processed WSI, and configured to provide the set of non-overlapping ROIs to a deep learning circuit; a deep learning circuit configured to generate an image-level probability that a member of the set of non-overlapping ROIs is a failure/abnormal pathology ROI using a CNN; and a classification circuit configured to generate a patient-level probability that the patient from which the region of tissue represented in the WSI was acquired is experiencing failure or non-failure based, at least in part, on the image-level probability.