Systems and methods for processing electronic images

An image processing method, comprising:

• receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient;
• generating a machine learning system by processing a plurality of training images, each training image comprising an image of human tissue and a label characterizing at least one of a slide morphology, a diagnostic value, a pathologist review outcome, and an analytic difficulty;
• automatically identifying, using the machine learning system, an area of interest of the target image by analyzing microscopic features extracted from multiple image regions in the target image;
• determining, using the machine learning system, a probability of a target feature being present in the area of interest of the target image based on an average probability;
• determining, using the machine learning system, a prioritization value, of a plurality of prioritization values, of the target image based on the probability of the target feature being present in the target image, the prioritization value comprising a first prioritization value determined based on preferences of a first user and a second prioritization value determined based on preferences of a second user;
• upon determining that the target feature comprises a feature in the target image indicating that further preparation is to be performed, then preparing a new slide for the target image prior to a user review;
• outputting, using the machine learning system, a plurality of digitized pathology images;
• and
• ordering, using the machine learning system, the digitized pathology images based on the plurality of prioritization values associated with the digitized pathology images, and a placement of the target image based on the prioritization value of the target image based on the target feature.

An image processing system, comprising:

• a memory storing instructions;
• and
• a processor configured to execute the instructions to perform operations comprising:
• receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient;
• generating a machine learning system by processing a plurality of training images, each training image comprising an image of human tissue and a label characterizing at least one of a slide morphology, a diagnostic value, a pathologist review outcome, and an analytic difficulty;
• automatically identifying, using the machine learning system, an area of interest of the target image by analyzing microscopic features extracted from multiple image regions in the target image;
• determining, using the machine learning system, a probability of a target feature being present in the area of interest of the target image based on an average probability;
• determining, using the machine learning system, a prioritization value, of a plurality of prioritization values, of the target image based on the probability of the target feature being present in the target image, the prioritization value comprising a first prioritization value determined based on preferences of a first user and a second prioritization value determined based on preferences of a second user;
• upon determining that the target feature comprises a feature in the target image indicating that further preparation is to be performed, then preparing a new slide for the target image prior to a user review;
• outputting, using the machine learning system, a plurality of digitized pathology images;
• and
• ordering, using the machine learning system, the digitized pathology images based on the plurality of prioritization values associated with the digitized pathology images, and a placement of the target image based on the prioritization value of the target image based on the target feature

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform an image processing method, the method comprising:

• receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient;
• generating a machine learning system by processing a plurality of training images, each training image comprising an image of human tissue and a label characterizing at least one of a slide morphology, a diagnostic value, a pathologist review outcome, and an analytic difficulty;
• automatically identifying, using the machine learning system, an area of interest of the target image by analyzing microscopic features extracted from multiple image regions in the target image;
• determining, using the machine learning system, a probability of a target feature being present in the area of interest of the target image based on an average probability;
• determining, using the machine learning system, a prioritization value, of a plurality of prioritization values, of the target image based on the probability of the target feature being present in the target image, the prioritization value comprising a first prioritization value determined based on preferences of a first user and a second prioritization value determined based on preferences of a second user;
• upon determining that the target feature comprises a feature in the target image indicating that further preparation is to be performed, then preparing a new slide for the target image prior to a user review;
• outputting, using the machine learning system, a plurality of digitized pathology images;
• and
• ordering, using the machine learning system, the digitized pathology images based on the plurality of prioritization values associated with the digitized pathology images, and a placement of the target image based on the prioritization value of the target image based on the target feature.