Systems and methods for processing electronic images to detect contamination in specimen preparations

A computer-implemented method for training a machine-learning model for processing digital images of a tissue specimen, the method comprising:

• providing, by one or more processors, one or more gathered and/or simulated sets of digital images of a plurality of tissue specimens to one or more machine-learning algorithms as one or more sets of training data;
• determining, by the one or more machine-learning algorithms, associations between the one or more gathered and/or simulated sets of digital images and one or more tissue features;
• modifying one or more of a layer, a weight, a synapse, or a node of the machine-learning model based on the associations between the one or more gathered and/or simulated sets of digital images and the one or more tissue features;
• and
• outputting, by the one or more processors, the machine-learning model, wherein the machine-learning model is trained to identify a sample tissue class of a contiguous tissue piece based on the one or more tissue features and the modified one or more of the layer, the weight, the synapse, or the node of the machine-learning model.

A system for processing digital images of a tissue specimen, the system comprising:

• a memory storing instruction and a trained machine-learning model trained to identify a presence of one or more external contaminants and/or a location of the one or more external contaminants present and output an external contaminant prediction;
• and
• a processor operatively connected to the memory and configured to execute the instructions to perform operations including:
• receiving, by a floater detection platform, the digital images of the tissue specimen;
• receiving, by the floater detection platform, data associated with a tissue type of the tissue specimen, the data including a tissue class;
• detecting, by the floater detection platform, one or more contiguous tissue pieces of the tissue specimen using an analysis of the digital images;
• extracting, by the floater detection platform, a plurality of features from each contiguous tissue piece of the one or more contiguous tissue pieces of the tissue specimen;
• providing, by the floater detection platform, the plurality of features from each contiguous tissue piece to a machine-learning model, wherein the machine-learning model has been trained, using one or more gathered and/or simulated sets of digital images of a plurality of tissue specimens, to identify a sample tissue class of each contiguous tissue piece based on the plurality of features;
• outputting, by the machine-learning model, a first sample tissue class of a first contiguous tissue piece;
• comparing, by the floater detection platform, the first sample tissue class of the first contiguous tissue piece with the tissue class of the tissue specimen to determine a tissue class mismatch;
• and
• generating, by the floater detection platform, an alert based on the tissue class mismatch.