You work as a machine learning specialist for a media sharing service. Healthcare professionals will use the media sharing service to share images of x-rays, MRIs, and other medical imagery. The accuracy of labelling these images is of primary importance, since the labelling will be used in auto diagnostic software. As your team builds the data repository to be used by your machine learning algorithms, you need to use human manual labellers. You have decided to use Amazon Ground Truth for this purpose. Since accuracy is of prime importance, you have decided to use the annotation consolidation feature of Ground Truth to ensure proper labelling of the medical images.
Which of the Ground Truth annotation consolidation functions should you use to ensure the accuracy of your labelling tasks? (Select TWO)

Answer options:

A.Bounding box
B.Semantic segmentation
C.Named entity
D.Output manifest
E.Mechanical turk

Answers: A, B
Option A is correct. The bounding box finds the most similar bounding boxes from workers and averages them, thus using the power of multiple workers to annotate your images more accurately.
Option B is correct. The semantic segmentation feature fuses the pixel annotations of multiple workers and applying a smoothing function to the image, thus using the power of multiple workers to annotate your images more accurately.
Option C is incorrect. The named entity feature is used with text annotation work, not image annotation.
Option D is incorrect. The Ground Truth output manifest allows the output of a labelling job to be used as the input to a machine learning model. This feature will not help ensure the accuracy of worker annotations.
Option E is incorrect. The Ground Truth Mechanical Turk feature gives you access to a large pool of labelling workers. While increasing the number of workers at your disposal, this feature will not help ensure the accuracy of worker annotations.
Reference:
Please see the Amazon SageMaker developer guide titled Annotation Consolidation, and the Amazon Machine Learning blog titled Use the wisdom of crowds with Amazon SageMaker Ground Truth to annotate data more accurately, and GitHub repository titled Amazon Sagemaker Examples Introduction to Ground Truth Labeling Jobs.