  The data-driven nature of deep learning (DL) models for semantic segmentation
requires a large number of pixel-level annotations. However, large-scale and
fully labeled medical datasets are often unavailable for practical tasks.
Recently, partially supervised methods have been proposed to utilize images
with incomplete labels in the medical domain. To bridge the methodological gaps
in partially supervised learning (PSL) under data scarcity, we propose Vicinal
Labels Under Uncertainty (VLUU), a simple yet efficient framework utilizing the
human structure similarity for partially supervised medical image segmentation.
Motivated by multi-task learning and vicinal risk minimization, VLUU transforms
the partially supervised problem into a fully supervised problem by generating
vicinal labels. We systematically evaluate VLUU under the challenges of
small-scale data, dataset shift, and class imbalance on two commonly used
segmentation datasets for the tasks of chest organ segmentation and optic
disc-and-cup segmentation. The experimental results show that VLUU can
consistently outperform previous partially supervised models in these settings.
Our research suggests a new research direction in label-efficient deep learning
with partial supervision.
