# Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation

arXiv: https://arxiv.org/abs/1806.05974 · HTML: https://mlsanity.com/paper/1806.05974/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [Tricks and Plugins to GBM on Images and Sequences](https://mlsanity.com/paper/2203.00761/) (2022)
- [Tricks and Plug-ins for Gradient Boosting in Image Classification](https://mlsanity.com/paper/2507.22842/) (2025)

## Abstract (arXiv)

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.