surgeRy

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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