Systems and methods for processing electronic images to determine testing for unstained specimens

A system for using a trained machine learning model for tissue analysis includes memory storing instructions, and at least one processor executing the instructions to perform operations comprising:

• receiving a collection of unstained digital histopathology slide images;
• running the trained machine learning model on one or more unstained digital histopathology slide images of the collection to infer a presence or an absence of a salient feature,
• wherein the trained machine learning model has been trained by processing a second collection of unstained and/or stained digital histopathology slide images and at least one synoptic annotation for one or more unstained and/or stained digital histopathology slide images of the second collection, wherein the processing comprises:
• virtually staining one or more unstained digital histopathology slide images of the second collection to a stain or using an image processing technique to un-stain one or more stained digital histopathology slide images of the second collection;
• and
• training a machine learning model to take as input one or more locations on a slide image to infer a presence of a salient label;
• determining at least one map based on output of the trained machine learning model, wherein the at least one map produces detection regions;
• and
• utilizing the at least one map, to determine where the salient feature is located, in one or more tests related to the collection of unstained digital histopathology slide images.

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for using a trained machine learning model for tissue analysis, the operations comprising:

• receiving a collection of unstained digital histopathology slide images;
• running a trained machine learning model on one or more unstained digital histopathology slide images to infer a presence or an absence of a salient feature,
• wherein the trained machine learning model has been trained by processing a second collection of unstained and/or stained digital histopathology slide images and at least one synoptic annotation for one or more unstained and/or stained digital histopathology slide images of the second collection, wherein the processing comprises:
• virtually staining one or more unstained digital histopathology slide images of the second collection to a stain or using an image processing technique to un-stain one or more stained digital histopathology slide images of the second collection;
• and
• training a machine learning model to take as input one or more locations on a slide image to infer a presence of a salient label;
• determining at least one map based on output of the trained machine learning model, wherein the at least one map produces detection regions;
• and
• utilizing the at least one map, to determine where the salient feature is located, in one or more tests related to the collection of unstained digital histopathology slide images.