Many recent machine learning approaches used in medical imaging are highly
reliant on large amounts of image and ground truth data. In the context of
object segmentation, pixel-wise annotations are extremely expensive to collect,
especially in video and 3D volumes. To reduce this annotation burden, we
propose a novel framework to allow annotators to simply observe the object to
segment and record where they have looked at with a \$200 eye gaze tracker. Our
method then estimates pixel-wise probabilities for the presence of the object
throughout the sequence from which we train a classifier in semi-supervised
setting using a novel Expected Exponential loss function. We show that our
framework provides superior performances on a wide range of medical image
settings compared to existing strategies and that our method can be combined
with current crowd-sourcing paradigms as well.Comment: 9 pages, 5 figues, MICCAI 2017 - LABELS Worksho