@inproceedings{lefolgoc2016lifted,
author = {Le Folgoc, Loic and Nori, Aditya and Ancha, Siddharth and Criminisi, Antonio},
title = {Lifted Auto-Context Forests for Brain Tumour Segmentation},
booktitle = {MICCAI Brain Lesion Workshop},
year = {2016},
month = {October},
abstract = {We revisit Auto-Context Forests for brain tumour segmentation in multi-channel magnetic resonance images, where semantic context is progressively built and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) improved generalization via an efficient node-splitting criterion based on hold-out estimates, (2) increased compactness at a tree-level, thereby yielding shallow discriminative ensembles trained orders of magnitude faster, and (3) guided semantic bagging that exposes latent data-space semantics captured by forest pathways. The proposed framework is practical: the per-layer training is fast, modular and robust. It was a top performer in the MICCAI 2016 BRATS (Brain Tumour Segmentation) challenge, and this paper aims to discuss and provide details about the challenge entry.},
publisher = {Springer, Cham},
url = {http://approjects.co.za/?big=en-us/research/publication/lifted-auto-context-forests-brain-tumour-segmentation/},
pages = {171-183},
}