Learning to Segment Skin Lesions from Noisy Annotations

Understand

Deep convolutional neural networks have driven substantial advancements in the automatic understanding of images.

• Requiring a large collection of images and their associated annotations is one of the main bottlenecks limiting the adoption of deep networks.
• In the task of medical image segmentation, requiring pixel-level semantic annotations performed by human experts exacerbate this difficulty.
• This paper proposes a new framework to train a fully convolutional segmentation network from a large set of cheap unreliable annotations and a small set of expert-level clean annotations.