Patch level severity determination method, slide level severity determination method, and computing system for performing same

A slide level severity determination method performed in a computing system comprising a first deep learning model which is an artificial neural network pre-trained, if each of partial images obtained by dividing a pathology slide image into a predetermined unit size is inputted, to output a determination result for the inputted partial image, the slide level severity determination method comprising:

• for each of a plurality of determination target partial images obtained by dividing a predetermined determination target pathology slide image into the unit size, determining an effect grade for the determination target partial image based on a determination result for the determination target partial image outputted by the first deep learning model receiving the determination target partial image;
• and
• determining a slide level severity grade for the whole determination target pathology slide image based on the effective grade for each of the plurality of determination target partial images constituting the determination target pathology slide image,
• wherein the determination result for the partial image outputted by the first deep learning model comprises a determination result for each pixel constituting the partial image, the determination result for each pixel is a likelihood value for each grade in a predetermined histological severity grading system with respect to a predetermined disease, and a predetermined grade score is pre-assigned to each grade in the severity grading system, and
• the determining of the effective grade for the determination target partial image based on the determination result for the determination target partial image outputted by the first deep learning model receiving the determination target partial image comprises:
• for each determination target pixel constituting the determination target partial image, determining the effective grade for the determination target pixel based on the likelihood value of each grade in the severity grading system of the determination target pixel and the grade score for each grade in the severity grading system;
• and
• determining the effective grade for the determination target partial image based on the effective grade of each determination target pixel constituting the determination target partial image.

A computing system comprising:

• a processor;
• and
• a memory,
• wherein the memory stores a computer program and a first deep learning model which is an artificial neural network pre-trained, if each of partial images obtained by dividing a pathology slide image into a predetermined unit size is inputted, to output a determination result for the inputted partial image,
• the computer program, when executed by the processor, controls the computing system to perform a slide level severity determination method,
• wherein the slide level severity determination method comprises:
• for each of a plurality of determination target partial images obtained by dividing a predetermined determination target pathology slide image into the unit size, determining an effect grade for the determination target partial image based on a determination result for the determination target partial image outputted by the first deep learning model receiving the determination target partial image;
• and
• determining a slide level severity grade for the whole determination target pathology slide image based on the effective grade for each of the plurality of determination target partial images constituting the determination target pathology slide image,
• wherein the determination result for the partial image outputted by the first deep learning model comprises a determination result for each pixel constituting the partial image, the determination result for each pixel is a likelihood value for each grade in a predetermined histological severity grading system with respect to a predetermined disease, and a predetermined grade score is pre-assigned to each grade in the severity grading system, and
• wherein the determining of the effective grade for the determination target partial image based on the determination result for the determination target partial image outputted by the first deep learning model receiving the determination target partial image comprises:
• for each determination target pixel constituting the determination target partial image, determining an effective grade for the determination target pixel based on the likelihood value of each grade in the severity grading system of the determination target pixel and the grade score for each grade in the severity grading system;
• and
• determining the effective grade for the determination target partial image based on the effective grade of each determination target pixel constituting the determination target partial image.