# An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation Source: [An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:53:10.657Z Authors: Lorenz Berger, Eoin Hyde, M. Jorge Cardoso, Sebastien Ourselin Published: 2017-09-08T16:17:55.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()