Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning
This work addresses the problem of reducing annotation costs for segmentation tasks, particularly in medical imaging, by enabling effective learning from weak labels, though it appears incremental in its approach.
The paper tackles the challenge of training segmentation models with weak labels like scribbles by introducing a self-supervised multi-scale consistency loss with an attention mechanism, achieving state-of-the-art performance on multiple medical and non-medical datasets.
Collecting large-scale medical datasets with fine-grained annotations is time-consuming and requires experts. For this reason, weakly supervised learning aims at optimising machine learning models using weaker forms of annotations, such as scribbles, which are easier and faster to collect. Unfortunately, training with weak labels is challenging and needs regularisation. Herein, we introduce a novel self-supervised multi-scale consistency loss, which, coupled with an attention mechanism, encourages the segmentor to learn multi-scale relationships between objects and improves performance. We show state-of-the-art performance on several medical and non-medical datasets. The code used for the experiments is available at https://vios-s.github.io/multiscale-pyag.