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: 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: 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: 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.

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.

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: A program implemented method converts complex graphics and picture images in binary form into bit map representations which include only that image data which is essential for its reproduction by an output printer with limited buffer memory. The method includes the steps of storing a group of rows or block of the binary image data at a time in an input buffer, then analyzing a byte at a time, the row segment of image data thereby dividing the row segment into a number of cell matrices. Based upon the analysis of cell matrices, the method generates bit map representations describing only the essential or black data. Each bit map representation generated has a height corresponding to the number of horizontal scan lines and a length which corresponds to the number of consecutive cell matrices detected as containing black data.