DIAGNOSIS OF A DISEASE CONDITION USING AN AUTOMATED DIAGNOSTIC MODEL
A method of identifying an object of interest can comprise obtaining first samples of an intensity distribution of one or more object of interest, obtaining second samples of an intensity distribution of confounder objects, transforming the first and second samples into an appropriate first space, performing dimension reduction on the transformed first and second samples, whereby the dimension reduction of the transformed first and second samples generates an object detector, transforming one or more of the digital images into the first space, performing dimension reduction on the transformed digital images, whereby the dimension reduction of the transformed digital images generates one or more reduced images, classifying one or more pixels of the one or more reduced images based on a comparison with the object detector, and identifying one or more objects of interest from the classified pixels.
1 . A system for diagnosing a disease condition in a patient, the system comprising:
one or more computer processors on one or more computers;
a computer readable medium on the one or more computers;
an object detection model stored on the computer readable medium, the object detection model comprising a set of parameters of a supervised procedure, wherein the object detection model is configured to output, based on an image, a probability that the image contains an object of interest at each of one or more locations within the image;
an automated diagnostic model stored on the computer readable medium, the automated diagnostic model comprising a set of parameters of a diagnostic model, wherein the automated diagnostic model is configured to output, based on the probability that the image contains the object of interest at each of one or more locations within the image, a probabilistic diagnosis of a disease condition depicted in the image; and
a diagnostic system stored on the computer readable medium, the diagnostic system containing instructions configured to cause the one or more computer processors to perform steps comprising:
receiving the image, the image containing a portion of a body of the patient,
passing the image to the object detection model,
receiving, from the object detection model, the probability that the image contains the object of interest at each of the one or more locations within the image,
passing, to the automated diagnostic model, the probability that the image contains the object of interest at each of the one or more locations within the image,
receiving, from the automated diagnostic model, the probabilistic diagnosis of the disease condition for the image, and
outputting the probabilistic diagnosis of the disease condition.