A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation

TL;DR

This paper introduces BoostNet, a deep gradient boosting network that enhances optic disc and cup segmentation by connecting prediction branches, leading to improved accuracy over existing methods.

Contribution

The paper proposes a novel gradient boosting framework for deep neural networks, with deformable units and deep supervision to improve segmentation performance.

Findings

BoostNet outperforms existing methods on ORIGA dataset.

Gradient boosting enhances learning capacity of segmentation network.

Deep-to-shallow stacking improves model performance gradually.

Abstract

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.