  Gastric cancer is the second leading cause of cancer-related deaths
worldwide, and the major hurdle in biomedical image analysis is the
determination of the cancer extent. This assignment has high clinical relevance
and would generally require vast microscopic assessment by pathologists. Recent
advances in deep learning have produced inspiring results on biomedical image
segmentation, while its outcome is reliant on comprehensive annotation. This
requires plenty of labor costs, for the ground truth must be annotated
meticulously by pathologists. In this paper, a reiterative learning framework
was presented to train our network on partial annotated biomedical images, and
superior performance was achieved without any pre-trained or further manual
annotation. We eliminate the boundary error of patch-based model through our
overlapped region forecast algorithm. Through these advisable methods, a mean
intersection over union coefficient (IOU) of 0.883 and mean accuracy of 91.09%
on the partial labeled dataset was achieved, which made us win the 2017 China
Big Data & Artificial Intelligence Innovation and Entrepreneurship
Competitions.
