Method and system for detecting pathological anomalies in a digital pathology image and method for annotating a tissue slide

A method performed by a computing system for detecting pathological anomalies in a digital pathology image, comprising:

• providing a digital pathology image to the computing system;
• and
• analyzing the digital pathology image using an identification module arranged on the computing system, wherein the identification module uses a machine learning module to execute:
• recognizing an object containing an abnormal image pattern using an identification model loaded in said identification module;
• and
• identifying whether the abnormal image pattern corresponds to a pathological anomaly using the identification model,
• wherein providing the identification model comprises:
• providing a tissue slide with pathological anomalies;
• selecting at least one biomarker and at least one fluorescence dye, or at least one fluorescent biomarker, specific for a respective pathological anomaly;
• staining the tissue slide with the at least one biomarker and the at least one fluorescence dye or, the at least one fluorescent biomarker, wherein said tissue slide is stained with multiple fluorescent dyes which emit light at different wavelengths;
• generating at least one first digital image of the tissue slide;
• staining the tissue slide with a first histochemical dye;
• generating at least one second digital image of the same tissue slide, wherein the tissue slide is stained with a second histochemical dye, said second histochemical dye is H&E;
• annotating the at least one second image of the tissue slide by layering the at least one first digital image and the at least one second digital image over another;
• providing the annotated images to the machine learning module;
• and
• training the identification model using different morphological features of the pathological anomalies to obtain the classifier, and
• wherein the step of analyzing the digital pathology image comprises analyzing the multiple fluorescent dyes on the same tissue slide.

A method for annotating a tissue slide image, comprising:

• providing a tissue slide with pathological anomalies;
• staining the tissue slide with at least one biomarker and at least one fluorescence dye, or at least one fluorescent biomarker, specific for a respective pathological anomaly, wherein said tissue slide is stained with multiple fluorescent dyes which emit light at different wavelengths;
• generating at least one first digital image of the tissue slide;
• staining the tissue slide with a first histochemical dye;
• generating at least one second digital image of the same tissue slide, wherein the tissue slide is stained with a second histochemical dye, said second histochemical dye is H&E;
• annotating the at least one second image of the tissue slide by using a stained region of the at least one first image of the tissue slide;
• and
• providing the annotated images to the machine learning module and training the identification model using different morphological features of the pathological anomalies to obtain the classifier,
• wherein said step of annotating comprises layering the at least one first image of the tissue slide and the at least one second image of the tissue slide over another and using the stained region of the at least one first image of the tissue slide as mask for cropping the at least one second image of the tissue slide.

A system for detecting pathological anomalies in a digital pathology image, comprising:

• a digital pathology image receiving module;
• and
• a digital pathology image analyzing module;
• wherein the digital pathology image analyzing module comprises an identification module, wherein the identification module comprises a machine learning module and an identification model,
• wherein the machine learning module comprises:
• a recognizing module for recognizing an object containing an abnormal image pattern using the identification model;
• and
• an identifying module for identifying whether the abnormal image pattern corresponds to a pathological anomaly using the identification model,
• wherein the identification model is provided by
• using a machine learning algorithm trained on a plurality of annotated digital pathology images;
• generating at least one first digital image of a tissue slide comprising stained morphological features of the pathological anomaly, wherein the tissue slide is stained with:
• a first histochemical dye, or
• at least one biomarker and at least one fluorescence dye, or
• at least one fluorescent biomarker;
• generating at least one second digital image of the same tissue slide, wherein the tissue slide is stained with a second histochemical dye, said second histochemical dye is H&E;
• annotating the at least one second digital image of the tissue slide by layering the at least one first digital image and the at least one second digital image over another;
• and
• providing the annotated images to the machine learning module and training the identification model using different morphological features of the pathological anomalies to obtain the classifier,
• wherein said tissue slide is stained with multiple fluorescent dyes which emit light at different wavelengths;
• and
• wherein the digital pathology image analyzing module is configured to analyze the multiple fluorescent dyes on the same tissue slide.