US 11,842,488 B2
Explainable AI (xAI) platform for computational pathology
Akif Burak Tosun, Pittsburgh, PA (US); Srinivas Chakra Chennubhotla, Pittsburgh, PA (US); and Jeffrey Louis Fine, Pittsburgh, PA (US)
Assigned to SpIntellx, Inc., Pittsburgh, PA (US)
Filed by SpIntellx, Inc., Pittsburgh, PA (US)
Filed on Jun. 17, 2022, as Appl. No. 17/843,309.
Application 17/843,309 is a continuation of application No. 16/819,866, filed on Mar. 16, 2020, granted, now 11,367,184, issued on Jun. 21, 2022.
Claims priority of provisional application 62/819,035, filed on Mar. 15, 2019.
Prior Publication US 2023/0142758 A1, May 11, 2023
Int. Cl. G16H 30/40 (2018.01); G06T 7/00 (2017.01); G06F 18/2431 (2023.01); G06V 10/778 (2022.01); G06V 20/69 (2022.01)
CPC G06T 7/0012 (2013.01) [G06F 18/2431 (2023.01); G06V 10/7784 (2022.01); G06V 20/69 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G16H 30/40 (2018.01); G06T 2200/24 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30061 (2013.01); G06T 2207/30068 (2013.01); G06T 2207/30096 (2013.01); G06T 2207/30242 (2013.01)] 31 Claims
OG exemplary drawing
 
1. 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.