Abstract: Techniques for classifying a human cutaneous tissue specimen are presented. The techniques may include obtaining a computer readable image of the human tissue sample and preprocessing the image. The techniques may include applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained. The techniques can also include applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, where the specimen level diagnosis includes at least one of: basal cell carcinoma, dermal nevus, or seborrheic keratosis. The techniques can include outputting the specimen level diagnosis.

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 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 classifying a human cutaneous tissue specimen are presented. The techniques may include obtaining a computer readable image of the human tissue sample and preprocessing the image. The techniques may include applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained. The techniques can also include applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, where the specimen level diagnosis includes at least one of: basal cell carcinoma, dermal nevus, or seborrheic keratosis. The techniques can include outputting the specimen level diagnosis.

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: Techniques for classifying a human cutaneous tissue specimen are presented. The techniques may include obtaining a computer readable image of the human tissue sample and preprocessing the image. The techniques may include applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained. The techniques can also include applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, where the specimen level diagnosis includes at least one of: basal cell carcinoma, dermal nevus, or seborrheic keratosis. The techniques can include outputting the specimen level diagnosis.

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.

Abstract: A method comprises: receiving, via a processor, an image depicting a tissue; quantifying, via the processor, the image based on: segmenting, via the processor, the image into a plurality of segments; identifying, via the processor, a plurality of histological elements in the segments; forming, via the processor, a network graph comprising a plurality of nodes, wherein the histological elements correspond to the nodes; measuring, via the processor, a feature of the network graph; performing, via the processor, a transformation on the image based on the feature; determining, via the processor, a non-parametric feature of the image based on the transformation; saving, via the processor, the non-parametric feature onto a database.

Abstract: The present disclosure generally relates to evaluating medical images. Some embodiments access stored medical images, provide a form template construction application to a coordinating user, and provide review applications to reviewing users. The form template construction application provides a tool for creating on a form template at least one user control for designating a region on a medical image and a tool for creating on a form template at least one user input for receiving diagnosis data about a medical image. The form template construction application distributes review forms based on the form template to the reviewing users. The reviewing users provide review data for each image, such an identification of a region and a corresponding diagnosis. A central server collects the review data from the reviewing users and stores it for use by the coordinating user.

Abstract: Techniques for classifying a human cutaneous tissue specimen are presented. The techniques may include obtaining a computer readable image of the human tissue sample and preprocessing the image. The techniques may include applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained. The techniques can also include applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, where the specimen level diagnosis includes at least one of: basal cell carcinoma, dermal nevus, or seborrheic keratosis. The techniques can include outputting the specimen level diagnosis.

Abstract: Techniques for classifying a human cutaneous tissue specimen are presented. The techniques may include obtaining a computer readable image of the human tissue sample and preprocessing the image. The techniques may include applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained. The techniques can also include applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, where the specimen level diagnosis includes at least one of: basal cell carcinoma, dermal nevus, or seborrheic keratosis. The techniques can include outputting the specimen level diagnosis.

Abstract: Presented are techniques for processing medical images. The techniques can include accessing a stored medical image and electronically representing a plurality of overlapping tiles that cover the medical image, each overlapping tile including a non-overlapping inner portion and an overlapping marginal portion. The techniques can also include in parallel, and individually for each of a plurality of the overlapping tiles: applying a segmentation process to identify objects in the at least one medical image, identifying inner object data representing at least one inner object that is contained within an inner portion of at least one tile, and identifying marginal object data representing at least one marginal object that overlaps a marginal portion of at least one tile. The techniques can also include merging at least some of the marginal object data to produce merged data, and outputting object data including the inner object data and the merged data.