Spatially Adaptive Random Forests

@article{Geremia2013SpatiallyAR,
  title={Spatially Adaptive Random Forests},
  author={Ezequiel Geremia and Bjoern H. Menze and Nicholas Ayache},
  journal={2013 IEEE 10th International Symposium on Biomedical Imaging},
  year={2013},
  pages={1344-1347}
}
Medical imaging protocols produce large amounts of multimodal volumetric images. The large size of the datasets contributes to the success of supervised discriminative methods for semantic image segmentation. Classifying relevant structures in medical images is challenging due to (a) the large size of data volumes, and (b) the severe class overlap in the feature space. Subsampling the training data addresses the first issue at the cost of discarding potentially useful image information… CONTINUE READING

Figures, Results, and Topics from this paper.

Key Quantitative Results

  • Additionally, SARF performed 90% faster than classical random forests applied without sub-sampling for comparable results.

Citations

Publications citing this paper.
SHOWING 1-10 OF 23 CITATIONS

A random forest guided tour

VIEW 4 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Medical Computer Vision: Algorithms for Big Data

  • Lecture Notes in Computer Science
  • 2015
VIEW 9 EXCERPTS
CITES BACKGROUND, METHODS & RESULTS
HIGHLY INFLUENCED

Interactive Segmentation of Glioblastoma for Post-surgical Treatment Follow-up

  • 2018 24th International Conference on Pattern Recognition (ICPR)
  • 2018
VIEW 1 EXCERPT

Hierarchical multi-scale supervoxel matching using random forests for automatic semi-dense abdominal image registration

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
  • 2017
VIEW 2 EXCERPTS
CITES BACKGROUND & METHODS

References

Publications referenced by this paper.
SHOWING 1-10 OF 12 REFERENCES

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2012
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Automatic Segmentation and Classification of Multiple Sclerosis in Multichannel MRI

  • IEEE Transactions on Biomedical Engineering
  • 2009
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL