Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers
This addresses the tedious annotation burden for physicians in medical imaging, though it is incremental as it builds on existing weakly-supervised methods.
The paper tackles the problem of polyp segmentation for colorectal cancer diagnosis by proposing a framework that uses weakly- and semi-supervised learning to reduce reliance on pixel-wise annotations, achieving results that outperform some fully supervised models on five datasets.
Polyp segmentation is a crucial step towards computer-aided diagnosis of colorectal cancer. However, most of the polyp segmentation methods require pixel-wise annotated datasets. Annotated datasets are tedious and time-consuming to produce, especially for physicians who must dedicate their time to their patients. We tackle this issue by proposing a novel framework that can be trained using only weakly annotated images along with exploiting unlabeled images. To this end, we propose three ideas to address this problem, more specifically our contributions are: 1) a novel sparse foreground loss that suppresses false positives and improves weakly-supervised training, 2) a batch-wise weighted consistency loss utilizing predicted segmentation maps from identical networks trained using different initialization during semi-supervised training, 3) a deformable transformer encoder neck for feature enhancement by fusing information across levels and flexible spatial locations. Extensive experimental results demonstrate the merits of our ideas on five challenging datasets outperforming some state-of-the-art fully supervised models. Also, our framework can be utilized to fine-tune models trained on natural image segmentation datasets drastically improving their performance for polyp segmentation and impressively demonstrating superior performance to fully supervised fine-tuning.