Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning
This addresses the challenge of reducing labor costs for pathologists in gastric cancer diagnosis by enabling segmentation with limited annotations, though it appears incremental as it builds on existing patch-based methods.
The paper tackles the problem of gastric tumor segmentation from biomedical images with partial annotations, achieving a mean intersection over union (IOU) of 0.883 and mean accuracy of 91.09% on a partial labeled dataset.
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.