L. Peter, O. Pauly, P. Chatelain, D. Mateus, N. Navab Scale-Adaptive Forest Training via an Efficient Feature Sampling Scheme Proceedings of the 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Munich, Germany, October 2015 (bib)
	In the context of forest-based segmentation of medical data, modeling the visual appearance around a voxel requires the choice of the scale at which contextual information is extracted, which is of crucial importance for the final segmentation performance. Building on Haar-like visual features, we introduce a simple yet effective modification of the forest training which automatically infers the most informative scale at each stage of the procedure. Instead of the standard uniform sampling during node split optimization, our approach draws candidate features sequentially in a fine-to-coarse fashion. While being very easy to implement, this alternative is free of additional parameters, has the same computational cost as a standard training and shows consistent improvements on three medical segmentation datasets with very different properties.
	This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.