A method comprising: executing, by a processor, a machine learning model that receives an input of a first image of an anatomical region of a patient depicting an organ having an outline and a second image comprising an overlay contouring the outline and further configured to predict a first confidence score indicating a likelihood of the overlay correctly contouring the outline, wherein the overlay is a contour, wherein the machine learning model is trained based on a set of third images depicting a set of second organs having a set of second outlines and a set of fourth images comprising a set of second overlays incorrectly contouring the set of second outlines, and wherein the machine learning model is further trained based on a set of fifth images depicting a set of third organs having a set of third outlines and a set of sixth images comprising a set of fourth overlays correctly contouring the set of third outlines, wherein the machine learning model comprises a discriminative network, and wherein the discriminative network generates a Class Activation Map, CAM, to determine contributions of localized regions of the contour to the first confidence score; and presenting, by the processor for display on a graphical user interface, the first image and a third overlay, and providing visual indications of the localized regions of the contour that contribute to the first confidence score executing, by a processor, a machine learning model that receives an input of a first image of an anatomical region of a patient depicting an organ having an outline and a second image comprising an overlay contouring the outline and further configured to predict a first confidence score indicating a likelihood of the overlay correctly contouring the outline, wherein the overlay is a contour, wherein the machine learning model is trained based on a set of third images depicting a set of second organs having a set of second outlines and a set of fourth images comprising a set of second overlays incorrectly contouring the set of second outlines, and wherein the machine learning model is further trained based on a set of fifth images depicting a set of third organs having a set of third outlines and a set of sixth images comprising a set of fourth overlays correctly contouring the set of third outlines, wherein the machine learning model comprises a discriminative network, and wherein the discriminative network generates a Class Activation Map, CAM, to determine contributions of localized regions of the contour to the first confidence score; and wherein the machine learning model is trained based on a set of third images depicting a set of second organs having a set of second outlines and a set of fourth images comprising a set of second overlays incorrectly contouring the set of second outlines, and wherein the machine learning model is further trained based on a set of fifth images depicting a set of third organs having a set of third outlines and a set of sixth images comprising a set of fourth overlays correctly contouring the set of third outlines, wherein the machine learning model comprises a discriminative network, and wherein the discriminative network generates a Class Activation Map, CAM, to determine contributions of localized regions of the contour to the first confidence score; and presenting, by the processor for display on a graphical user interface, the first image and a third overlay, and providing visual indications of the localized regions of the contour that contribute to the first confidence score <EOS>