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Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation
[article]
2018
arXiv
pre-print
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 volumetric data sets, such as CT scans. Our contribution is threefold: 1) We propose a boosted sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in a more informative loss. This results in a significant
arXiv:1806.05974v2
fatcat:2scfv4up7zfi5ks7qbitbzi6by