Abstract: One example method includes receiving a digital image of a needle core prostate biopsy, displaying, using a display device, a magnified portion of the digital image, obtaining, from a deep learning model, Gleason scores corresponding to patches of the magnified portion of the digital image, and displaying, using the display device, a superimposed overlay on the magnified portion of the digital image based on the Gleason scores and corresponding confidence values of the Gleason scores, the superimposed overlay comprising one or more outlines corresponding one or more Gleason scores associated with the magnified portion of the digital image and comprising image patches having colors based on a Gleason score of the Gleason scores corresponding to an underlying portion of the magnified portion of the digital image and a confidence value of the corresponding Gleason score.

Abstract: One example method for AI prediction of prostate cancer outcomes involves receiving an image of prostate tissue; assigning Gleason pattern values to one or more regions within the image using an artificial intelligence Gleason grading model, the model trained to identify Gleason patterns on a patch-by-patch basis in a prostate tissue image; determining relative areal proportions of the Gleason patterns within the image; assigning at least one of a risk score or risk group value to the image based on the determined relative areal proportions; and outputting at least one of the risk score or the risk group value.

Abstract: A method comprises: receiving, in an offboard computer system separate from a vehicle, a user request for a onetime passcode, the user request including a vehicle identification number (VIN) of the vehicle and a user selection of a function set among multiple functions of the vehicle; generating, by the offboard computer system, a onetime password for the function set based on at least (i) a shared key associated with the function set and the VIN, and (ii) an offboard counter value associated with the function set and the VIN; forming, by the offboard computer system, the onetime passcode from at least the onetime password and a code identifying the function set; and presenting, by the offboard computer system, the onetime passcode in response to the user request.

Abstract: Methods and systems that facilitate automatic generation of Application Programming Interface (API) specification from web traffic. Methods include obtaining a plurality of API requests and responses to the plurality of API requests. Methods include processing these API requests and responses to API requests to identify one or more attributes, such as, for example, variables, query parameters, response status codes, and response schemas. Methods include identifying variables using a tree data structure to represent resource paths. Methods include identifying query parameters based on resource items in resource paths. Methods include determining that the API call does not conform to the API specification by comparing one or more attributes of the API call with the attributes of the API specification.

Abstract: One example method includes receiving a digital image of a needle core prostate biopsy, displaying, using a display device, a magnified portion of the digital image, obtaining, from a deep learning model, Gleason scores corresponding to patches of the magnified portion of the digital image, and displaying, using the display device, a superimposed overlay on the magnified portion of the digital image based on the Gleason scores and corresponding confidence values of the Gleason scores, the superimposed overlay comprising one or more outlines corresponding one or more Gleason scores associated with the magnified portion of the digital image and comprising image patches having colors based on a Gleason score of the Gleason scores corresponding to an underlying portion of the magnified portion of the digital image and a confidence value of the corresponding Gleason score.

Abstract: One example method for AI prediction of prostate cancer outcomes involves receiving an image of prostate tissue; assigning Gleason pattern values to one or more regions within the image using an artificial intelligence Gleason grading model, the model trained to identify Gleason patterns on a patch-by-patch basis in a prostate tissue image; determining relative areal proportions of the Gleason patterns within the image; assigning at least one of a risk score or risk group value to the image based on the determined relative areal proportions; and outputting at least one of the risk score or the risk group value.

Abstract: Methods and systems that facilitate automatic generation of Application Programming Interface (API) specification from web traffic. Methods include obtaining a plurality of API requests and responses to the plurality of API requests. Methods include processing these API requests and responses to API requests to identify one or more attributes, such as, for example, variables, query parameters, response status codes, and response schemas. Methods include identifying variables using a tree data structure to represent resource paths. Methods include identifying query parameters based on resource items in resource paths. Methods include determining that the API call does not conform to the API specification by comparing one or more attributes of the API call with the attributes of the API specification.

Abstract: Methods and systems that facilitate automatic generation of Application Programming Interface (API) specification from web traffic. Methods include obtaining a plurality of API requests and responses to the plurality of API requests. Methods include processing these API requests and responses to API requests to identify one or more attributes, such as, for example, variables, query parameters, response status codes, and response schemas. Methods include identifying variables using a tree data structure to represent resource paths. Methods include identifying query parameters based on resource items in resource paths. Methods include determining that the API call does not conform to the API specification by comparing one or more attributes of the API call with the attributes of the API specification.

Abstract: Methods and systems that facilitate automatic generation of Application Programming Interface (API) specification from web traffic. Methods include obtaining a plurality of API requests and responses to the plurality of API requests. Methods include processing these API requests and responses to API requests to identify one or more attributes, such as, for example, variables, query parameters, response status codes, and response schemas. Methods include identifying variables using a tree data structure to represent resource paths. Methods include identifying query parameters based on resource items in resource paths. Methods include determining that the API call does not conform to the API specification by comparing one or more attributes of the API call with the attributes of the API specification.

Abstract: Methods and systems that facilitate automatic generation of Application Programming Interface (API) specification from web traffic. Methods include obtaining a plurality of API requests and responses to the plurality of API requests. Methods include processing these API requests and responses to API requests to identify one or more attributes, such as, for example, variables, query parameters, response status codes, and response schemas. Methods include identifying variables using a tree data structure to represent resource paths. Methods include identifying query parameters based on resource items in resource paths. Methods include determining that the API call does not conform to the API specification by comparing one or more attributes of the API call with the attributes of the API specification.