# Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation Source: [Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:06:03.000Z Authors: Lorenz Berger, Eoin Hyde, Matt Gibb, Nevil Pavithran, Garin Kelly, Faiz Mumtaz, S\\'ebastien Ourselin Published: 2018-06-13T19:42:20.000Z ## Abstract 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 training speed up and improves learning performance for image segmentation. 2) We propose a novel algorithm for boosting the SGD learning rate schedule by adaptively increasing and lowering the learning rate, avoiding the need for extensive hyperparameter tuning. 3) 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]()