Abstract: In some aspects, the described systems and methods provide for validating performance of a model trained on a plurality of annotated pathology images. A pathology image is accessed. Frames are generated using the pathology image. Each frame in the set includes a distinct portion of the pathology image. Reference annotations are received from one or more users. The reference annotations describe at least one of a plurality of tissue or cellular characteristic categories for one or more frames in the set. Each frame in the set is processed using the trained model to generate model predictions. The model predictions describe at least one of the tissue or cellular characteristic categories for the processed frame. Performance of the trained model is validated based on determining a degree of association between the reference annotations and the model predictions for each frame and/or across all frames in the set of frames.

Abstract: In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.

Abstract: In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.

Abstract: In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.

Abstract: In some aspects, the described systems and methods provide for validating performance of a model trained on a plurality of annotated pathology images. A pathology image is accessed. Frames are generated using the pathology image. Each frame in the set includes a distinct portion of the pathology image. Reference annotations are received from one or more users. The reference annotations describe at least one of a plurality of tissue or cellular characteristic categories for one or more frames in the set. Each frame in the set is processed using the trained model to generate model predictions. The model predictions describe at least one of the tissue or cellular characteristic categories for the processed frame. Performance of the trained model is validated based on determining a degree of association between the reference annotations and the model predictions for each frame and/or across all frames in the set of frames.

Abstract: Techniques for performing diagnostic assessments based on digital pathology data are disclosed. In one particular embodiment, the techniques may be realized as a method for performing a diagnostic assessment based on digital pathology data comprising obtaining first digital pathology data comprising intensity information, the first digital pathology data being associated with a plurality of regions of interest in a biological sample; applying first machine learning models to the first digital pathology data, the first machine learning models identifying first regions of interest among the plurality of regions of interest based on the intensity information; applying second machine learning models to the first digital pathology data, the second machine learning models identifying at least one pattern associated with at least one of the first regions of interest; generating a diagnostic assessment based on the first regions of interest and the at least one pattern.

Abstract: In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.

Abstract: In some aspects, the described systems and methods provide for a method for predicting tissue characteristics for a pathology image. A statistical model trained on multiple annotated pathology images is used. Each of the training pathology images includes an annotation describing tissue characteristics for one or more portions of the image. The method includes accessing a pathology image for predicting tissue characteristics. A trained statistical model is retrieved from a storage device. A set of patches is defined from the pathology image. Each of the patches in the set includes a subset of pixels from the corresponding pathology image. The set of patches is processed using the trained statistical model to predict respective annotations for each patch in the set. The predicted annotations are stored on the storage device.

Abstract: In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.

Abstract: In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.

Abstract: In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.

Abstract: The invention provides enhancements for the use of favorites during a Web browsing session. A first enhancement identifies when a user is adding a favorite to his favorites list and auto-suggests a category under which the favorite could be stored. A second enhancement allows a user to review his favorites list and see a summary of feed content (RSS or other standard) on each feed enabled page on his favorites list, without requiring the user to link to the page in question. A third enhancement allows the user to view an manipulate the feed in an independent display window.

Abstract: The invention provides enhancements for the use of favorites during a Web browsing session. A first enhancement identifies when a user is adding a favorite to his favorites list and auto-suggests a category under which the favorite could be stored. A second enhancement allows a user to review his favorites list and see a summary of feed content (RSS or other standard) on each feed enabled page on his favorites list, without requiring the user to link to the page in question. A third enhancement allows the user to view an manipulate the feed in an independent display window.

Abstract: Product order and shipping information received via email messages is automatically aggregated for ready user review. Once the user is authenticated, authorization to access their email mailbox is obtained and the email message headers of their entails are analyzed to identify those messages of interest. The bodies of the email messages of interest are parsed to extract the product order and shipping information which is stored and presented for display to the user typically grouped by individual product thus greatly simplifying user review of orders. The aggregated product order and shipping information can be augmented with additional information such as shipping status, delivery status, a product image, and/or a last date that the product can be returned.

Abstract: The invention provides two enhancements for the use of favorites during a Web browsing session. A first enhancement identifies when a user is adding a favorite to his favorites list and auto-suggests a category under which the favorite could be stored. A second enhancement allows a user to review his favorites list and see a summary of feed content (RSS or other standard) on each feed enabled page on his favorites list, without requiring the user to link to the page in question.

Abstract: Product order and shipping information received via email messages is automatically aggregated for ready user review. Once the user is authenticated, authorization to access their email mailbox is obtained and the email message headers of their entails are analyzed to identify those messages of interest. The bodies of the email messages of interest are parsed to extract the product order and shipping information which is stored and presented for display to the user typically grouped by individual product thus greatly simplifying user review of orders. The aggregated product order and shipping information can be augmented with additional information such as shipping status, delivery status, a product image, and/or a last date that the product can be returned.

Abstract: Product order and shipping information received via email messages is automatically aggregated for ready user review. Once the user is authenticated, authorization to access their email mailbox is obtained and the email message headers of their emails are analyzed to identify those messages of interest. The bodies of the email messages of interest are parsed to extract the product order and shipping information which is stored and presented for display to the user typically grouped by individual product thus greatly simplifying user review of orders.

Abstract: Product order and shipping information received via email messages is automatically aggregated for ready user review. Once the user is authenticated, authorization to access their email mailbox is obtained and the email message headers of their entails are analyzed to identify those messages of interest. The bodies of the email messages of interest are parsed to extract the product order and shipping information which is stored and presented for display to the user typically grouped by individual product thus greatly simplifying user review of orders. The aggregated product order and shipping information can be augmented with additional information such as shipping status, delivery status, a product image, and/or a last date that the product can be returned.

Abstract: Product order and shipping information received via email messages is automatically aggregated for ready user review. Once the user is authenticated, authorization to access their email mailbox is obtained and the email message headers of their emails are analyzed to identify those messages of interest. The bodies of the email messages of interest are parsed to extract the product order and shipping information which is stored and presented for display to the user typically grouped by individual product thus greatly simplifying user review of orders. The aggregated product order and shipping information can be augmented with additional information such as shipping status, delivery status, a product image, and/or a last date that the product can be returned.

Abstract: The invention provides enhancements for the use of favorites during a Web browsing session. A first enhancement identifies when a user is adding a favorite to his favorites list and auto-suggests a category under which the favorite could be stored. A second enhancement allows a user to review his favorites list and see a summary of feed content (RSS or other standard) on each feed enabled page on his favorites list, without requiring the user to link to the page in question. A third enhancement allows the user to view an manipulate the feed in an independent display window.