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 \200eyegazetracker.Ourmethodthenestimatespixel−wiseprobabilitiesforthepresenceoftheobjectthroughoutthesequencefromwhichwetrainaclassifierinsemi−supervisedsettingusinganovelExpectedExponentiallossfunction.Weshowthatourframeworkprovidessuperiorperformancesonawiderangeofmedicalimagesettingscomparedtoexistingstrategiesandthatourmethodcanbecombinedwithcurrentcrowd−sourcingparadigmsaswell.
