Systems and methods for analyzing electronic images for quality control

A computer-implemented method for processing electronic images, the method comprising:

• receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of human or animal tissue and/or an image algorithmically generated to replicate human or animal tissue;
• determining a quality control (QC) machine learning model configured to predict a quality designation based on one or more artifacts;
• providing the digital image as an input to the QC machine learning model;
• receiving the quality designation for the digital image as an output from the QC machine learning model;
• determining a quality assurance (QA) machine learning model different than the QC machine learning model;
• providing the digital image as an input to the QA machine learning model based on the quality designation being an approved quality designation;
• receiving a disease designation for the digital image as an output from the QA machine learning model;
• receiving an external designation for one of the digital image or the target specimen, the external designation generated by a user and comprising a disease property selected from at least one of a cancer detection, a cancer grade, a cancer origin, a diagnosis, a presence or absence of a microorganism, a specimen type, a cancer type, a cancer status, a tumor size, a lesions risk level, or a grade;
• evaluating the external designation by comparing the disease designation to the external designation;
• and
• outputting a comparison result based on evaluating the external designation by comparing the disease designation to the external designation.

A computer-implemented method for processing electronic images, the method comprising:

• receiving at least one digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of human or animal tissue and/or an image algorithmically generated to replicate human or animal tissue;
• determining a quality assurance (QA) machine learning model configured to predict a disease designation based on one or more biomarkers;
• providing the digital image as an input to the QA machine learning model;
• receiving the disease designation for the digital image as an output from the QA machine learning model;
• receiving an external designation for one of the digital image or the target specimen, the external designation generated by a user and comprising a disease property selected from at least one of a cancer detection, a cancer grade, a cancer origin, a diagnosis, a presence or absence of a microorganism, a specimen type, a cancer type, a cancer status, a tumor size, a lesions risk level, or a grade;
• evaluating the external designation by comparing the disease designation to the external designation;
• and
• outputting a comparison result based on evaluating the external designation by comparing the disease designation to the external designation.

A system for processing electronic images, the system comprising:

• at least one memory storing instructions;
• and
• at least one processor executing the instructions to perform operations comprising:
• receiving at least one digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of human or animal tissue and/or an image algorithmically generated to replicate human or animal tissue;
• determining a quality assurance (QA) machine learning model configured to predict a disease designation based on one or more biomarkers;
• providing the digital image as an input to the QA machine learning model;
• receiving the disease designation for the digital image as an output from the QA machine learning model;
• receiving an external designation for one of the digital image or the target specimen, the external designation generated by a user and comprising a disease property selected from at least one of a cancer detection, a cancer grade, a cancer origin, a diagnosis, a presence or absence of a microorganism, a specimen type, a cancer type, a cancer status, a tumor size, a lesions risk level, or a grade;
• evaluating the external designation by comparing the disease designation to the external designation;
• and
• outputting a comparison result based on evaluating the external designation by comparing the disease designation to the external designation.