Whole tissue classifier for histology biopsy slides

摘要

Disclosed is a computer implemented method for fully automated tissue diagnosis that trains a region of interest (ROI) classifier in a supervised manner, wherein labels are given only at a tissue level, the training using a multiple-instance learning variant of backpropagation, and trains a tissue classifier that uses the output of the ROI classifier. For a given tissue, the method finds ROIs, extracts feature vectors in each ROI, applies the ROI classifier to each feature vector thereby obtaining a set of probabilities, provides the probabilities to the tissue classifier and outputs a final diagnosis for the whole tissue.

主权项

1. A computer-implemented method of whole tissue classification steps of:
training a Multi-Layer Perceptron (MLP) classifier in a supervised manner wherein labels are given only at a tissue level, the training using a multiple-instance learning variant of backpropagation, wherein an input feature vector that generates the largest output value within all regions of interest (ROI) is back-propagated; training a tissue classifier with an output of the MLP classifier; for a given tissue image:
segmenting the tissue image into ROIs;extracting a vector of features from each of the ROIs;applying the MLP classifier to the vector of features of each ROI thereby obtaining a set of probabilities;providing the probabilities to a tissue classifier; andoutputting a diagnosis of the whole-tissue.