A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation
This work addresses a domain-specific problem in medical image analysis for automated diagnosis, with incremental improvements over existing methods.
The paper tackled the problem of optic disc and cup segmentation in fundus images by proposing BoostNet, a gradient boosting network that connects prediction branches, achieving superior results on the ORIGA dataset.
Segmentation of optic disc (OD) and optic cup (OC) is critical in automated fundus image analysis system. Existing state-of-the-arts focus on designing deep neural networks with one or multiple dense prediction branches. Such kind of designs ignore connections among prediction branches and their learning capacity is limited. To build connections among prediction branches, this paper introduces gradient boosting framework to deep classification model and proposes a gradient boosting network called BoostNet. Specifically, deformable side-output unit and aggregation unit with deep supervisions are proposed to learn base functions and expansion coefficients in gradient boosting framework. By stacking aggregation units in a deep-to-shallow manner, models' performances are gradually boosted along deep to shallow stages. BoostNet achieves superior results to existing deep OD and OC segmentation networks on the public dataset ORIGA.