  Segmentation using deep learning has shown promising directions in medical
imaging as it aids in the analysis and diagnosis of diseases. Nevertheless, a
main drawback of deep models is that they require a large amount of pixel-level
labels, which are laborious and expensive to obtain. To mitigate this problem,
weakly supervised learning has emerged as an efficient alternative, which
employs image-level labels, scribbles, points, or bounding boxes as
supervision. Among these, image-level labels are easier to obtain. However,
since this type of annotation only contains object category information, the
segmentation task under this learning paradigm is a challenging problem. To
address this issue, visual salient regions derived from trained classification
networks are typically used. Despite their success to identify important
regions on classification tasks, these saliency regions only focus on the most
discriminant areas of an image, limiting their use in semantic segmentation. In
this work, we propose a manifold driven attention-based network to enhance
visual salient regions, thereby improving segmentation accuracy in a weakly
supervised setting. Our method generates superior attention maps directly
during inference without the need of extra computations. We evaluate the
benefits of our approach in the task of segmentation using a public benchmark
on skin lesion images. Results demonstrate that our method outperforms the
state-of-the-art GradCAM by a margin of ~22% in terms of Dice score.

Computer Vision and Pattern Recognition