Abstract: A computing system includes a processor; an electronic network; and a memory having stored thereon computer-executable instructions that, when executed, cause the computing system to: process segmented tile images by: (i) predicting a respective biomarker classification, and (ii) predicting a respective tissue classification; determine, based on (i) and (ii), a predicted presence of biomarkers; and transmit the predicted presence. A non-transitory computer-readable medium includes computer-executable instructions that, when executed by a processor, cause a computer to: process segmented tile images by: (i) predicting a respective biomarker classification, and (ii) predicting a respective tissue classification; determine, based on (i) and (ii), a predicted presence of biomarkers; and transmit the predicted presence.

Abstract: A system for identifying biomarkers in a digital image of a Hematoxylin and Eosin-stained slide of a target tissue includes a processor and an electronic network; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: process segmented tile images determine a predicted biomarker presence; and transmit the predicted presence. A non-transitory computer-readable medium includes a set of computer-executable instructions that, when executed by one or more processors, cause a computer to: process segmented tile images; determine a predicted biomarker presence; and transmit the predicted presence. A computer-implemented method includes processing segmented tile images; determining a predicted biomarker presence; and transmitting the predicted presence.

Abstract: A system for identifying biomarkers in a digital image of a Hematoxylin and Eosin-stained slide of a target tissue includes a processor and an electronic network; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: process segmented tile images determine a predicted biomarker presence; and transmit the predicted presence. A non-transitory computer-readable medium includes a set of computer-executable instructions that, when executed by one or more processors, cause a computer to: process segmented tile images; determine a predicted biomarker presence; and transmit the predicted presence. A computer-implemented method includes processing segmented tile images; determining a predicted biomarker presence; and transmitting the predicted presence.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Abstract: A method for qualifying a specimen prepared on one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and providing associated unstained slides for subsequent nucleic acid analysis is provided.

Abstract: A method for determining tumor block sufficiency for generating one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and determining a number of H&E slide for satisfying a desired total nucleic yield is provided.

Abstract: A method for predicting an expected yield of nucleic acid from tumor cells within a dissection boundary on a hematoxylin and eosin (H&E) slide is provided.

Abstract: A method for qualifying a specimen prepared on one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and providing associated unstained slides for subsequent nucleic acid analysis is provided.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Abstract: A method for qualifying a specimen prepared on one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and providing associated unstained slides for subsequent nucleic acid analysis is provided.

Abstract: A method for determining tumor block sufficiency for generating one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and determining a number of H&E slide for satisfying a desired total nucleic yield is provided.

Abstract: A method for predicting an expected yield of nucleic acid from tumor cells within a dissection boundary on a hematoxylin and eosin (H&E) slide is provided.

Abstract: Techniques for generating an overlay map on a digital medical image of a slide are provided, and include cell detection and tissue classification processes. Techniques include receiving a medical image, separating the image into tiles, and performing tile classifications and tissue classifications based on a multi-tile analysis. Techniques additionally include identifying cell objects in the image, separating the image into and displaying polygons identifying the cell objects and cell classifications. Generated displays may be overlays over the initial digital image.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.