Systems and methods for processing electronic images for generalized disease detection

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 a generalized machine learning model generated by processing a plurality of first training images to predict at least one cancer characteristic;
• receiving a plurality of second training images, wherein the first training images and the second training images comprise images of tissue specimens and/or images algorithmically generated to replicate tissue specimens a quantity of second training images being insufficient to generate a machine learning model that meets a threshold for outputting one or more characteristics of target images for a specialized task;
• receiving a plurality of target specialized attributes each related to a respective second training image of the plurality of second training images;
• generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of second training images and the respective target specialized attributes, the specialized machine learning model meeting the threshold for outputting the one or more characteristics of target images for the specialized task, the specialized machine learning model being generated in accordance with a feature extraction scheme by generating additional layers to an existing set of layers of the generalized machine learning model and generating weights for the additional layers based on the plurality of second training images and the target specialized attributes;
• receiving a target image corresponding to a target specimen;
• applying the specialized machine learning model to the target image to determine at least one characteristic of the target image;
• and
• outputting the at least one characteristic of the target image.

A non-transitory computer-readable medium storing instructions that, when executed by processor, cause the processor to perform operations for processing electronic images, the operations comprising:

• receiving a generalized machine learning model generated by processing a plurality of first training images to predict at least one cancer characteristic;
• receiving a plurality of second training images, wherein the first training images and the second training images comprise images of tissue specimens and/or images algorithmically generated to replicate tissue specimens a quantity of second training images being insufficient to generate a machine learning model that meets a threshold for outputting one or more characteristics of target images for a specialized task;
• receiving a plurality of target specialized attributes each related to a respective second training image of the plurality of second training images;
• generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of second training images and the respective target specialized attributes, the specialized machine learning model meeting the threshold for outputting the one or more characteristics of target images for the specialized task, the specialized machine learning model being generated in accordance with a feature extraction scheme by generating additional layers to an existing set of layers of the generalized machine learning model and generating weights for the additional layers based on the plurality of second training images and the target specialized attributes;
• receiving a target image corresponding to a target specimen;
• applying the specialized machine learning model to the target image to determine at least one characteristic of the target image;
• and
• outputting the at least one characteristic of the target image.