Machine Learning Based Classification of Cytology Samples from Whole-Slide Images or Cytology Patches
AssigneesMayo Foundation for Medical Education and Research, University of Massachusetts
InventorsMichael Levy, Neil B. Marya, Patrick D. Powers, Rondell P. Graham
Life of this patent
Estimating the term…
Abstract
Computer-aided detection (“CADe”) and/or computer-aided diagnosis (“CADx”) of biliary tract diseases or other pathologies is provided using a machine learning-based analysis of cytology data. In general, a machine learning model-based framework allows cytopathologists to perform a targeted and abbreviated review of select patches extracted from whole-slide images, or in some instances entire whole-slide images, while maintaining interpretive accuracy.
Claims19
A method for classifying cytology patches with a computer system, comprising:
- (a) accessing cytology data with the computer system, wherein the cytology data comprise images patches extracted from whole-slide images of a cytology sample;
- (b) accessing a neural network with the computer system, wherein the neural network has been trained on training data to classify image patches as being associated with different disease classifications;
- (c) inputting the cytology data to the neural network using the computer system, generating classified feature data as an output, wherein the classified feature data indicate a classification of the image patches in the cytology data as one of the different disease classifications;
- and
- (d) presenting the classified feature data to a user by the computer system.
A method for computer-aided diagnosis of whole-slide images of a cytology sample, comprising:
- (a) accessing a whole-slide image with a computer system, the whole-slide image depicting a cytology sample;
- (b) accessing a machine learning model with the computer system, wherein the machine learning model has been trained on training data to classify whole-slide images as being positive or negative for containing a disease condition;
- (c) inputting the whole-slide image to the machine learning model using the computer system, generating whole-slide image (WSI) classified feature data as an output, wherein the WSI classified feature data indicate whether the whole-slide image is one of positive or negative for containing a disease condition;
- and
- (d) presenting the WSI classified feature data to a user by the computer system.
Dependencies are inferred from the claim text.