An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
This work addresses the problem of efficient training for semantic segmentation in medical imaging, offering an incremental improvement over existing methods.
The paper tackles the challenge of training deep convolutional neural networks for semantic segmentation on large, sparse datasets like 3D medical images by proposing an adaptive sampling scheme that uses a-posterior error maps to focus on difficult regions, resulting in new state-of-the-art results on the VISCERAL Anatomy benchmark.
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.