Explainable ai (xai) platform for computational pathology

A system for performing explainable pathological analysis of medical images, the system comprising:

• a first processor;
• and
• a first memory in electrical communication with the first processor, and comprising instructions that, when executed by a processing unit that comprises the first processor or a second processor, and that is in electronic communication with a memory module that comprises the first memory or a second memory, program the processing unit to:
• for a region of interest (ROI) in a whole slide image (WSI) of a tissue, identify features of a plurality of feature types, wherein at least one feature type is at least partially indicative of a pathological condition of the tissue within the ROI;
• operate as a classifier, trained to classify an image using features of the plurality of feature types into one of a plurality of classes of tissue conditions, to:
• (i) classify the ROI into a class within the plurality of classes, and (ii) designate to the ROI a label indicating a tissue condition associated with the class and with the tissue in the ROI;
• store explanatory information about the designation of the label, the explanatory information comprising information about the identified features;
• and
• display:
• (i) at least a portion of the WSI with boundary of the ROI highlighted, (ii) the label designated to the ROI, and (iii) a user interface (UI) comprising:
• (a) a first UI element for providing to a user access to the stored explanatory information, and (b) one or more additional UI elements enabling the user to provide feedback on the designated label.

A system for distributing cases among a group of pathologists, the system comprising:

• a first processor;
• and
• a first memory in electrical communication with the first processor, and comprising instructions that, when executed by a processing unit that comprises the first processor or a second processor, and that is in electronic communication with a memory module that comprises the first memory or a second memory, program the processing unit to:
• for each one of a plurality of cases, process a corresponding whole slide image (WSI) of a tissue wherein, to process the WSI, the instructions program the processing unit to:
• identify one or more regions of interest (ROls) in the WSI, each ROI having designated thereto a respective diagnostic label indicating a condition of a tissue in the ROI;
• for each ROI, compute a respective confidence score for the respective designation;
• compute for the WSI:
• (i) a severity score based on the respective diagnostic labels designated to the one or more ROls in the WSI;
• and (ii) a confidence level based on the respective confidence scores for the one or more ROls;
• store as explanatory information, the severity score, the confidence level, and the respective confidence scores;
• if the severity score is at or above a specified threshold severity score, transmit the WSI to a rush pathologist in the group of pathologists;
• otherwise, if the confidence level is at or below a specified threshold confidence level, transmit the WSI to a subspecialist in the group of pathologists;
• and
• otherwise, transmit the case to a generalist pathologist in the group of pathologists.

A system for ground truth labeling of images used for training a classifier, the system comprising:

• a first processor;
• and
• a first memory in electrical communication with the first processor, and comprising instructions that, when executed by a processing unit that comprises the first processor or a second processor, and that is in electronic communication with a memory module that comprises the first memory or a second memory, program the processing unit to:
• obtain a whole slide image (WSI) of a tissue;
• identify one or more regions of interest (ROls) in the WSI, wherein identification of an ROI comprises:
• (i) marking in the WSI, superpixels of at least two types, one type corresponding to hematoxylin stained tissue and another type corresponding to eosin stained tissue;
• and (ii) marking segments of pixels of a first type to define an enclosed region as the ROI;
• display, in a sequence, one or more ROls;
• and
• for each ROI:
• display one or more UI elements, a first UI element providing or affirming a respective ground-truth label to be assigned to the ROI;
• and
• in response to the user interacting using the first UI element, designate the respective ground-truth label to the ROI and storing the ROI in a training corpus.