documentation page here
deep learning training
Common tasks for data augmentation of images, segmentations and points.
Appropriate for cascaded models.
Installing
The pre-release version of the package can be pulled from GitHub using the devtools package:
# install.packages("devtools")
devtools::install_github("stnava/surgeRy", build_vignettes=TRUE)this lets you access vignettes via:
vignette(package='surgeRy')which will list the current vignettes.
For developers
The repository includes a Makefile to facilitate some common tasks.
Running tests
$ make test. Requires the testthat package. You can also specify a specific test file or files to run by adding a “file=” argument, like $ make test file=logging. testthat::test_package() will do a regular-expression pattern match within the file names (ignoring the test- prefix and the .R file extension).
Updating documentation
$ make doc. Requires the roxygen2 package.
References to the concepts used here
Cephalometric Landmark Regression with Convolutional Neural Networks on 3D Computed Tomography Data ( a review paper )
Numerical Coordinate Regression with Convolutional Neural Networks
Human pose regression by combining indirect part detection and contextual information (section 3.2)
Automatic 3d cephalometric annotation system using shadowed 2d image-based machine learning
