Abstract: Techniques for training a first electronic neural network classifier to identify a presence of a particular property in a novel supra-image while ignoring a spurious correlation of the presence of the particular property with a presence of an extraneous property are presented.

Abstract: Techniques for determining a presence of a pathology property in a supra-image are presented. The techniques can include receiving an electronic evaluation supra-image; providing the electronic evaluation supra-image to an electronic neural network that has been trained, using a training corpus of training supra-images and on an electronic computer, to determine the presence of the pathology property in a supra-image, each training supra-image including at least one image, each image corresponding to a plurality of components, wherein each training supra-image of the training corpus is associated with a respective electronic label indicating whether the pathology property is present, where the training corpus is sufficient to train the electronic neural network to determine a presence of the pathology property; receiving from the electronic neural network an output indicative of whether the pathology property is present in the evaluation supra-image; and providing the output.

Abstract: Techniques of automated quality control for digital pathology whole slide images are presented. The techniques include obtaining a thumbnail image derived from a whole slide image of a pathology slide; determining whether the whole slide image includes an artifact in a first class of artifacts by providing the thumbnail image to an electronic neural network trained to detect artifacts in the first class of artifacts by analyzing a plurality of labeled training thumbnail images; generating a tissue mask representing tissue depicted in the thumbnail image; determining whether the whole slide image includes an artifact in a second class of artifacts by performing a comparison using the tissue mask; and providing an indication of whether the whole slide image includes an artifact in the first class of artifacts or an artifact in the second class of artifacts.

Abstract: Techniques for training a first electronic neural network classifier to identify a presence of a particular property in a novel supra-image while ignoring a spurious correlation of the presence of the particular property with a presence of an extraneous property are presented.

Abstract: Techniques for stain normalization image processing for digitized biological tissue images are presented. The techniques include obtaining a digitized biological tissue image; applying to at least a portion of the digitized biological tissue image an at least partially computer implemented convolutional neural network trained using a training corpus including a plurality of pairs of images, where each pair of images of the plurality of pairs of images includes a first image restricted to a lightness axis of a color space and a second image restricted to at least one of: a first color axis of the color space and a second color axis of the color space, such that the applying causes an output image to be produced; and providing the output image.

Abstract: Computer-implemented techniques for classifying a tissue specimen are presented. The techniques include obtaining an image of the tissue specimen; segmenting the image into a first plurality of segments; selecting a second plurality of segments that include at least one region of interest; applying an electronic convolutional neural network trained by a training corpus including a set of pluralities of tissue sample image segments, each of the pluralities of tissue sample image segments labeled according to one of a plurality of primary pathology classes, where the plurality of primary pathology classes consist of a plurality of majority primary pathology classes, where the plurality of majority primary pathology classes collectively include a majority of pathologies according to prevalence, and a class for tissue sample image segments not in the plurality of majority primary pathology classes, such that a primary pathology classification is output; and providing the primary pathology classification.

Abstract: Computer-implemented techniques for classifying a tissue specimen are presented. The techniques include obtaining an image of the tissue specimen; segmenting the image into a first plurality of segments; selecting a second plurality of segments that include at least one region of interest; applying an electronic convolutional neural network trained by a training corpus including a set of pluralities of tissue sample image segments, each of the pluralities of tissue sample image segments labeled according to one of a plurality of primary pathology classes, where the plurality of primary pathology classes consist of a plurality of majority primary pathology classes, where the plurality of majority primary pathology classes collectively include a majority of pathologies according to prevalence, and a class for tissue sample image segments not in the plurality of majority primary pathology classes, such that a primary pathology classification is output; and providing the primary pathology classification.

Abstract: Techniques for stain normalization image processing for digitized biological tissue images are presented. The techniques include obtaining a digitized biological tissue image; applying to at least a portion of the digitized biological tissue image an at least partially computer implemented convolutional neural network trained using a training corpus including a plurality of pairs of images, where each pair of images of the plurality of pairs of images includes a first image restricted to a lightness axis of a color space and a second image restricted to at least one of: a first color axis of the color space and a second color axis of the color space, such that the applying causes an output image to be produced; and providing the output image.