Fast Boosting Trees for Classification, Pose Detection, and Boundary Detection on a GPU
Abstract
Discriminative classifiers are often the computational bottleneck in medical imaging applications such as foreground/background classification, 3D pose detection, and boundary delineation. To overcome this bottleneck, we propose a fast technique based on boosting tree classifiers adapted for GPU computation. Unlike standard tree-based algorithms, our method does not have any recursive calls which makes it GPU-friendly. The algorithm is integrated into an optimized Hierarchical Detection Network (HDN) for 3D pose detection and boundary detection in 3D medical images. On desktop GPUs, we demonstrate an 80× speedup in simple classification of Liver in MRI volumes, and 30× speedup in multi-object localization of fetal head structures in ultrasound images, and 10× speedup on 2.49 mm accurate Liver boundary detection in MRI.
Cite
Text
Birkbeck et al. "Fast Boosting Trees for Classification, Pose Detection, and Boundary Detection on a GPU." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2011. doi:10.1109/CVPRW.2011.5981802Markdown
[Birkbeck et al. "Fast Boosting Trees for Classification, Pose Detection, and Boundary Detection on a GPU." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2011.](https://mlanthology.org/cvprw/2011/birkbeck2011cvprw-fast/) doi:10.1109/CVPRW.2011.5981802BibTeX
@inproceedings{birkbeck2011cvprw-fast,
title = {{Fast Boosting Trees for Classification, Pose Detection, and Boundary Detection on a GPU}},
author = {Birkbeck, Neil and Sofka, Michal and Zhou, Shaohua Kevin},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2011},
pages = {36-41},
doi = {10.1109/CVPRW.2011.5981802},
url = {https://mlanthology.org/cvprw/2011/birkbeck2011cvprw-fast/}
}