R/data.R

In ayanglab/How-to-Perform-Technical-Systematic-Review-And-Meta-Analysis-Tutorial: What the Package Does (One Line, Title Case)

#' Metric performance data
#'
#' @description a dataframe with the following columns:
#' \describe{
#'           \item{method}{comparison method to evaluate the model performance.
#'           If the method is the proposed study, it is labelled 'proposed'.
#'           'ZF' means zero-filling}
#'           \item{acceleration}{the acceleration ratio under which the metric
#'           value is calculated}
#'           \item{paper}{which paper the data is collected}
#'           \item{dataset}{the testing dataset used to calculate the metric.
#'           If unknown, it will be 'dataset0'}
#'           \item{metric}{which metric}
#'           \item{value}{the value for the metric of the method}
#'           \item{}{}
#' }
#' @author Yutong Chen
"metric_val"

#' Change the display names of features
#'
#' @description a dataframe containing:
#' \describe{
#'           \item{original}{the original labels}
#'           \item{new}{the labels to display}
#' }
#' @author Yutong Chen
"recode_dict"

#' Configurate aesthetic parameters
#'
#' @description a list containing:
#' \describe{
#'         \item{fontsize}{the font size of all graphical elements except
#'         `ggplot2::geom_text` and `ggrepel::geom_text_repel`}
#'         \item{point_fontsize}{the font size of labelled text in the graph by
#'         `geom_text` and `geom_text_repel`}
#'         \item{legend_point_size}{the point size in legend symbol, if the value is
#'         discrete}
#'         \item{normal_shape}{shape of points, must be between 21 and 24}
#'         \item{font_fam}{font family}
#'         \item{npg_pal}{a vector of color palettes}
#'         \item{transparency}{color transparency}
#'         \item{shape_ops}{the shapes for dots if multiple shapes are required}
#'         \item{color_vec}{a named vector of colors to plot for each year from
#'         2017 to 2020}
#' }
#' @author Yutong Chen
"config"

#' Design traits of different studies
#'
#' @description a dataframe with the following columns:
#' \describe{
#'           \item{GAN.based}{logical vector, whether the study uses GAN}
#'           \item{PI}{logical vector, whether the study uses parallel imaging}
#'           \item{acceleration}{string, acceleration ratios across all
#'           experimental conditions}
#'           \item{architecture}{string, deep neural network architecture}
#'           \item{augmentation}{string, data augmentation technique}
#'           \item{better}{logical, whether the performance exceeds all the
#'           comparison methods listed in the study}
#'           \item{category}{string (factor), whether the study is 'unrolled
#'           optimisation' or 'end-to-end'}
#'           \item{channel.merging}{string, how channel/coil merging is
#'           performed if the study uses parallel imaging}
#'           \item{channel.number}{string, number of channels in the input
#'           image}
#'           \item{comparison}{string, comparison methods}
#'           \item{computation.time}{string, GPU computation time per
#'           reconstructed image in seconds}
#'           \item{country}{string, the country of the institute where the
#'           first author of the study is affliated to}
#'           \item{data.consistency}{logical, whether data consistency layer as
#'           in DC-CNN is used}
#'           \item{dataset}{string, public dataset in the study}
#'           \item{dimension}{numeric, the input dimension of the image, can be
#'           2, 3 or 4}
#'           \item{ground.truth}{string, how the ground truth is obtained}
#'           \item{input.domain}{string, the input domain of the image}
#'           \item{input.size}{string, the size of the input}
#'           \item{limitation}{string, limitation of the study reported by the
#'           authors}
#'           \item{loss}{string, loss function}
#'           \item{mask}{string, undersampling mask}
#'           \item{metric}{string, metric used to assess reconstruction performance}
#'           \item{modality}{string, the imaging sequence, e.g. T1, T2 weighted}
#'           \item{name}{string, the name of the model proposed}
#'           \item{novelty}{string, novelty mentioned by the authors}
#'           \item{open.source}{string, the url to the source code}
#'           \item{optimiser}{string, the type of optimiser used}
#'           \item{package}{string, deep learning framework, e.g. tensorflow,
#'           pytorch}
#'           \item{parameter.number}{string, the number of parameters}
#'           \item{region}{string, anatomical region in the image dataset,
#'           e.g. brain, knee}
#'           \item{residual}{logical, whether residual learning is applied}
#'           \item{result}{string, descriptions of reconstruction results}
#'           \item{result.table}{string, whether result table is provided for
#'           quantitative analysis}
#'           \item{spatial.domain}{string, whether spatial domain was used}
#'           \item{strength}{string, the strength of the model}
#'           \item{testing.data.pathology}{logical, whether the testing dataset
#'           contains pathological features}
#'           \item{testing.data.size}{string, sample size of the testing
#'           dataset}
#'           \item{testing.mode}{string, mode of testing, retrospective or
#'           prospective}
#'           \item{testing.number}{numeric, number of scans used in testing}
#'           \item{training.data.pathology}{logical, whether the training dataset
#'           contains pathological features}
#'           \item{training.data.size}{string, sample size of the training
#'           dataset}
#'           \item{training.number}{numeric, number of scans used in training}
#'           \item{unet.like}{logical, whether unet-like architecture is used}
#'           \item{unrolled}{logical, whether unrolled optimisation is used}
#'           \item{validation.data.size}{string, sample size of the validation
#'           dataset}
#'           \item{zero.filled}{logical, whether zero-filling is used as a
#'           comparison method}
#'           \item{others}{string, other information}
#'           \item{ID}{string, ID for each study used in this review paper}
#'           \item{year}{string, publication year according to the 'publish or
#'           perish' website}
#'           \item{journal}{string, in which journal the paper is found}
#'           \item{supervised}{logical, whether supervised learning is used}
#'           \item{institute}{logical, the institute which first author of the
#'           study is affliated to}
#'           \item{full_GT}{logical, whether fully sampled ground truth is
#'           available}
#'           \item{adam}{logical, whether adam optimiser is used}
#'           \item{central_mask}{logical, whether the mask samples the center
#'           of the image fully}
#'           \item{max_acc}{numeric, maximal acceleration ratio}
#'           \item{MSE_loss}{logical, whether mean squared error loss is used}
#'           \item{L1_loss}{logical, whether L1 loss is used}
#'           \item{L2_loss}{logical, whether L2 loss is used}
#'           \item{complex_inp}{logical, whether the input is complex signal}
#'           \item{public_code}{logical, whether the code is published}
#'           \item{public_data}{logical, whether the data is published}
#'           \item{reproducibility}{string, reproducibility of the study}
#'           \item{autoencoder}{logical, whether autoencoder is used}
#'           \item{MLP}{logical, whether multi-layer perceptron is used}
#'           \item{stack_conv}{logical, whetther stacked convolution is used}
#'           \item{model_structure}{string, architecture of the model}
#'           \item{cluster}{string, raw cluster output from `mclust::Mclust`}
#'           \item{new_cluster}{string, annotation of the raw cluster}
#' }
#' **NB**: If a particular study uses multiple methods for a particular trait,
#' each method is separated by a comma. A bracket may follow each method
#' describing the dataset that the method is applied in the study.
#' @author Yutong Chen
"revdat"