An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

TL;DR

This paper introduces an adaptive sampling method using error maps to efficiently train deep CNNs for semantic segmentation on large datasets, notably improving performance on 3D medical images.

Contribution

The paper presents a novel adaptive sampling scheme, a dual path CNN architecture, and demonstrates state-of-the-art results on the VISCERAL benchmark.

Findings

Improved training efficiency on large, sparse datasets.

Achieved state-of-the-art results on VISCERAL benchmark.

Effective focus on difficult regions enhances segmentation accuracy.

Abstract

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large data sets, such as three-dimensional medical images. We propose an adaptive sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in improved learning. Our contribution is threefold: 1) We give a detailed description of the proposed sampling algorithm to speed up and improve learning performance on large images. We propose a deep dual path CNN that captures information at fine and coarse scales, resulting in a network with a large field of view and high resolution outputs. We show that our method is able to attain new state-of-the-art results on the VISCERAL Anatomy benchmark.