The current framework is a tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond.
Biomedical challenges have become the de facto standard for benchmarking biomedical image analysis algorithms. While the number of challenges is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature.
Given these shortcomings, the current framework aims to enable fast and wide adoption of comprehensively analyzing and visualizing the results of single-task and multi-task challenges. This approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques.
Requires R version >= 3.5.2 (https://www.r-project.org).
Further, a recent version of Pandoc (>= 1.12.3) is required. RStudio (https://rstudio.com) automatically includes this so you do not need to download Pandoc if you plan to use rmarkdown from the RStudio IDE, otherwise you’ll need to install Pandoc for your platform (https://pandoc.org/installing.html). Finally, if you want to generate a PDF report you will need to have LaTeX installed (e.g. MiKTeX, MacTeX or TinyTeX).
To get the latest released version (master branch) of the R package from GitHub:
if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools")
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("Rgraphviz", dependencies = TRUE)
devtools::install_github("wiesenfa/challengeR", dependencies = TRUE)If you are asked whether you want to update installed packages and you type “a” for all, you might need administrator permissions to update R core packages. You can also try to type “n” for updating no packages. If you are asked “Do you want to install from sources the packages which need compilation? (Yes/no/cancel)”, you can safely type “no”.
If you get warning messages (in contrast to error messages), these might not be problematic and you can try to proceed. If you encounter errors during the setup, looking into the “Troubleshooting” section might be worth it.
For Linux users: Some system libraries might be missing. Check the output in the R console for further hints carefully during the installation of packages.
Copyright (c) German Cancer Research Center (DKFZ). All rights reserved.
challengeR is available under license GPLv2 or any later version.
If you use this software for a publication, please cite:
Wiesenfarth, M., Reinke, A., Landman, B.A., Eisenmann, M., Aguilera Saiz, L., Cardoso, M.J., Maier-Hein, L. and Kopp-Schneider, A. Methods and open-source toolkit for analyzing and visualizing challenge results. Sci Rep 11, 2369 (2021). https://doi.org/10.1038/s41598-021-82017-6
Each of the following steps has to be run to generate the report: (1) Load package, (2) load data, (3) perform ranking, (4) perform bootstrapping and (5) generation of the report
You can find R scripts for quickstart in the directory “vignettes”. An overview of all available plots is provided in the “Visualizations” vignette demonstrating the use of their corresponding plot functions as well.
Here, we provide a step-by-step guide that leads you to your final report.
Load package
library(challengeR)Data requires the following columns:
- task identifier in case of multi-task challenges (string or numeric)
- test case identifier (string or numeric)
- algorithm identifier (string or numeric)
- metric value (numeric)
In case of missing metric values, a missing observation has to be provided (either as blank field or “NA”).
For example, in a challenge with 2 tasks, 2 test cases and 2 algorithms, where in task “T2”, test case “case2”, algorithm “A2” didn’t give a prediction (and thus NA or a blank field for missing value is inserted), the data set might look like this:
| Task | TestCase | Algorithm | MetricValue |
|---|---|---|---|
| T1 | case1 | A1 | 0.266 |
| T1 | case1 | A2 | 0.202 |
| T1 | case2 | A1 | 0.573 |
| T1 | case2 | A2 | 0.945 |
| T2 | case1 | A1 | 0.372 |
| T2 | case1 | A2 | 0.898 |
| T2 | case2 | A1 | 0.908 |
| T2 | case2 | A2 | NA |
If you have assessment data at hand stored in a csv file (if you want to use simulated data, skip the following code line) use
data_matrix <- read.csv(file.choose()) # type ?read.csv for helpThis allows to choose a file interactively, otherwise replace file.choose() by the file path (in style “/path/to/dataset.csv”) in quotation marks.
In the following, simulated data is generated instead for illustration purposes (skip the following code chunk if you have already loaded data). The data is also stored as “inst/extdata/data_matrix.csv” in the repository.
if (!requireNamespace("permute", quietly = TRUE)) install.packages("permute")
n <- 50
set.seed(4)
strip <- runif(n,.9,1)
c_ideal <- cbind(task="c_ideal",
rbind(
data.frame(alg_name="A1",value=runif(n,.9,1),case=1:n),
data.frame(alg_name="A2",value=runif(n,.8,.89),case=1:n),
data.frame(alg_name="A3",value=runif(n,.7,.79),case=1:n),
data.frame(alg_name="A4",value=runif(n,.6,.69),case=1:n),
data.frame(alg_name="A5",value=runif(n,.5,.59),case=1:n)
))
set.seed(1)
c_random <- data.frame(task="c_random",
alg_name=factor(paste0("A",rep(1:5,each=n))),
value=plogis(rnorm(5*n,1.5,1)),case=rep(1:n,times=5)
)
strip2 <- seq(.8,1,length.out=5)
a <- permute::allPerms(1:5)
c_worstcase <- data.frame(task="c_worstcase",
alg_name=c(t(a)),
value=rep(strip2,nrow(a)),
case=rep(1:nrow(a),each=5)
)
c_worstcase <- rbind(c_worstcase,
data.frame(task="c_worstcase",alg_name=1:5,value=strip2,case=max(c_worstcase$case)+1)
)
c_worstcase$alg_name <- factor(c_worstcase$alg_name,labels=paste0("A",1:5))
data_matrix <- rbind(c_ideal, c_random, c_worstcase)Code differs slightly for single- and multi-task challenges.
In case of a single-task challenge use
# Use only task "c_random" in object data_matrix
dataSubset <- subset(data_matrix, task=="c_random")
challenge <- as.challenge(dataSubset,
# Specify which column contains the algorithms,
# which column contains a test case identifier
# and which contains the metric value:
algorithm = "alg_name", case = "case", value = "value",
# Specify if small metric values are better
smallBetter = FALSE)Instead, for a multi-task challenge use
# Same as above but with 'by="task"' where variable "task" contains the task identifier
challenge <- as.challenge(data_matrix,
by = "task",
algorithm = "alg_name", case = "case", value = "value",
smallBetter = FALSE)Different ranking methods are available, choose one of them:
- for “aggregate-then-rank” use (here: take mean for aggregation)
ranking <- challenge%>%aggregateThenRank(FUN = mean, # aggregation function,
# e.g. mean, median, min, max,
# or e.g. function(x) quantile(x, probs=0.05)
na.treat = 0, # either "na.rm" to remove missing data,
# set missings to numeric value (e.g. 0)
# or specify a function,
# e.g. function(x) min(x)
ties.method = "min" # a character string specifying
# how ties are treated, see ?base::rank
) - alternatively, for “rank-then-aggregate” with arguments as above (here: take mean for aggregation)
ranking <- challenge%>%rankThenAggregate(FUN = mean,
ties.method = "min"
)- alternatively, for test-then-rank based on Wilcoxon signed rank test
ranking <- challenge%>%testThenRank(alpha = 0.05, # significance level
p.adjust.method = "none", # method for adjustment for
# multiple testing, see ?p.adjust
na.treat = 0, # either "na.rm" to remove missing data,
# set missings to numeric value (e.g. 0)
# or specify a function, e.g. function(x) min(x)
ties.method = "min" # a character string specifying
# how ties are treated, see ?base::rank
)Perform bootstrapping with 1000 bootstrap samples using one CPU
set.seed(123, kind = "L'Ecuyer-CMRG")
ranking_bootstrapped <- ranking%>%bootstrap(nboot = 1000)If you want to use multiple CPUs (here: 8 CPUs), use
library(doParallel)
library(doRNG)
registerDoParallel(cores = 8)
registerDoRNG(123)
ranking_bootstrapped <- ranking%>%bootstrap(nboot = 1000, parallel = TRUE, progress = "none")
stopImplicitCluster()Generate report in PDF, HTML or DOCX format. Code differs slightly for single- and multi-task challenges.
ranking_bootstrapped %>%
report(title = "singleTaskChallengeExample", # used for the title of the report
file = "filename",
format = "PDF", # format can be "PDF", "HTML" or "Word"
latex_engine = "pdflatex", #LaTeX engine for producing PDF output. Options are "pdflatex", "lualatex", and "xelatex"
clean = TRUE #optional. Using TRUE will clean intermediate files that are created during rendering.
) Argument file allows for specifying the output file path as well, otherwise the working directory is used. If file is specified but does not have a file extension, an extension will be automatically added according to the output format given in format. Using argument clean=FALSE allows to retain intermediate files, such as separate files for each figure.
If argument “file” is omitted, the report is created in a temporary folder with file name “report”.
Same as for single-task challenges, but additionally consensus ranking (rank aggregation across tasks) has to be given.
Compute ranking consensus across tasks (here: consensus ranking according to mean ranks across tasks)
# See ?relation_consensus for different methods to derive consensus ranking
meanRanks <- ranking%>%consensus(method = "euclidean")
meanRanks # note that there may be ties (i.e. some algorithms have identical mean rank)Generate report as above, but with additional specification of consensus ranking
ranking_bootstrapped %>%
report(consensus = meanRanks,
title = "multiTaskChallengeExample",
file = "filename",
format = "PDF", # format can be "PDF", "HTML" or "Word"
latex_engine = "pdflatex"#LaTeX engine for producing PDF output. Options are "pdflatex", "lualatex", and "xelatex"
)The consensus ranking is given according to mean ranks across tasks if method=“euclidean” where in case of ties (equal ranks for multiple algorithms) the average rank is used, i.e. ties.method=“average”.
In this section we provide an overview of issues that the users reported and how they were solved.
While trying to install the current version of the repository:
devtools::install_github("wiesenfa/challengeR", dependencies = TRUE)The following warning showed up in the output:
WARNING: Rtools is required to build R packages, but is not currently installed.Therefore, Rtools was installed via a separate executable: https://cran.r-project.org/bin/windows/Rtools/ and the warning disappeared.
Actually there is no need of installing Rtools, it is not really used in the toolkit. Insted, choose not to install it when it is asked. See comment in the installation section:
“If you are asked whether you want to update installed packages and you type “a” for all, you might need administrator rights to update R core packages. You can also try to type “n” for updating no packages. If you are asked “Do you want to install from sources the packages which need compilation? (Yes/no/cancel)”, you can safely type “no”.”
Installing the current version of the tool from GitHub failed.
The error message was:
byte-compile and prepare package for lazy loading
Error: (converted from warning) package 'ggplot2' was built under R version 3.6.3
Execution halted
ERROR: lazy loading failed for package 'challengeR'
* removing 'C:/Users/.../Documents/R/win-library/3.6/challengeR'
* restoring previous 'C:/Users/.../Documents/R/win-library/3.6/challengeR'
Error: Failed to install 'challengeR' from GitHub:
(converted from warning) installation of package 'C:/Users/.../AppData/Local/Temp/Rtmp615qmV/file4fd419555eb4/challengeR_0.3.1.tar.gz' had non-zero exit statusThe problem was that some of the packages that were built under R3.6.1 had been updated, but the current installed version was still R3.6.1.
The solution was to update R3.6.1 to R3.6.3. Another way would have been to reset the single packages to the versions built under R3.6.1.
Installing the current version of the tool from GitHub failed.
devtools::install_github("wiesenfa/challengeR", dependencies = TRUE)The error message was:
Error: .onLoad failed in loadNamespace() for 'pkgload', details:
call: loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]])
error: there is no package called ‘backports’The problem was that the packages ‘backports’ had not been installed.
The solution was to install ‘backports’ manually.
install.packages("backports")While trying to install the package after running the following commands:
if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools")
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("Rgraphviz", dependencies = TRUE)
devtools::install_github("wiesenfa/challengeR", dependencies = TRUE)The error message was:
ERROR:
1: In file(con, "r") :
URL 'https://bioconductor.org/config.yaml': status was 'SSL connect error'
2: packages ‘BiocVersion’, ‘Rgraphviz’ are not available (for R version 3.6.1)The solution was to restart RStudio.
While generating the PDF with MiKTeX (2.9), the following error showed up:
fatal pdflatex - gui framework cannot be initializedThere is an issue with installing missing packages in LaTeX.
Open your MiKTeX Console –> Settings, select “Always install missing packages on-the-fly”. Then generate the report. Once the report is generated, you can reset the settings to your preferred ones.
While generating the PDF with MiKTeX (2.9):
ranking_bootstrapped %>%
report(title = "singleTaskChallengeExample", # used for the title of the report
file = "filename",
format = "PDF", # format can be "PDF", "HTML" or "Word"
latex_engine = "pdflatex", #LaTeX engine for producing PDF output. Options are "pdflatex", "lualatex", and "xelatex"
clean = TRUE #optional. Using TRUE will clean intermediate files that are created during rendering.
) The following error showed up:
output file: filename.knit.md
"C:/Program Files/RStudio/bin/pandoc/pandoc" +RTS -K512m -RTS filename.utf8.md --to latex --from markdown+autolink_bare_uris+tex_math_single_backslash --output filename.tex --self-contained --number-sections --highlight-style tango --pdf-engine pdflatex --variable graphics --lua-filter "C:/Users/adm/Documents/R/win-library/3.6/rmarkdown/rmd/lua/pagebreak.lua" --lua-filter "C:/Users/adm/Documents/R/win-library/3.6/rmarkdown/rmd/lua/latex-div.lua" --variable "geometry:margin=1in"
Error: LaTeX failed to compile filename.tex. See https://yihui.org/tinytex/r/#debugging for debugging tips.
Warning message:
In system2(..., stdout = if (use_file_stdout()) f1 else FALSE, stderr = f2) :
'"pdflatex"' not foundThe solution was to restart RStudio.
- Ensure reproducibility with parallel bootstrapping (T29361)
- Fix NaN values cause error (T28746)
- Fix Bars and dots don’t match in podium plot (T29167)
- Fix y-axis of blob plots always scaled to 5 (T28966)
- Fix ggplot warning in various places of the report (T28710)
- Fix error when all metric values are the same (T28453)
- Fix wrong number of algorithms shown in report summary (T28465)
- Fix error raised in case there are more tasks than algorithms contained in the dataset (T28193)
- Drop restriction that at least three algorithms are required for bootstrapping (T28194)
- Avoid blank pages in PDF report when bootstrapping is disabled (T28201)
- Handle tasks having only one case for bootstrapping (T28202)
- Update citation (T28210)
- Revision of the underlying data structure
- Roxygen documentation for main functionality
- Vignettes for quickstart and overview of available plots demonstrating the use of their corresponding plot functions
- Introduction of unit tests (package coverage >70%)
- Troubleshooting section covering potential issues during setup
- Finally: Extensive bug fixes and improvements (for a complete overview please check the Phabricator tasks)
- Force line break to avoid that authors exceed the page in generated PDF reports
- Correct names of authors
- Refactoring
- Major bug fix release
- Bug fixes
- Automatic insertion of missings
- Bug fixes
- Reports for subsets (top list) of algorithms: Use
e.g.
subset(ranking_bootstrapped, top=3) %>% report(...)(orsubset(ranking, top=3) %>% report(...)for report without bootstrap results) to only show the top 3 algorithms according to the chosen ranking methods, whereranking_bootstrappedandrankingobjects as defined in the example. Line plot for ranking robustness can be used to check whether algorithms performing well in other ranking methods are excluded. Bootstrapping still takes entire uncertainty into account. Podium plot and ranking heatmap neglect excluded algorithms. Only available for single-task challenges (for multi-task challenges not sensible because each task would contain a different set of algorithms). - Reports for subsets of tasks: Use
e.g.
subset(ranking_bootstrapped, tasks=c("task1", "task2","task3")) %>% report(...)to restrict report to tasks “task1”, “task2”,"task3. You may want to recompute the consensus ranking before usingmeanRanks=subset(ranking, tasks=c("task1", "task2", "task3"))%>%consensus(method = "euclidean")
- Introduction in reports now mentions e.g. ranking method, number of test cases,…
- Function
subset()allows selection of tasks after bootstrapping, e.g.subset(ranking_bootstrapped,1:3) report()functions gain argumentcolors(default:default_colors). Change e.g. tocolors=viridisLite::infernowhich “is designed in such a way that it will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. It is also designed to be perceived by readers with the most common form of color blindness.” See packageviridisfor further similar functions.
- Improved layout in case of many algorithms and tasks (while probably still not perfect)
- Consistent coloring of algorithms across figures
report()function can be applied to ranked object before bootstrapping (and thus excluding figures based on bootstrapping), i.e. in the exampleranking %>% report(...)- bug fixes
The developer team includes members from both division of Intelligent Medical Systems (IMSY) and Biostatistics at the German Cancer Research Center (DKFZ):
- Manuel Wiesenfarth
- Annette Kopp-Schneider
- Annika Reinke
- Matthias Eisenmann
- Laura Aguilera Saiz
- Elise Récéjac
- Lena Maier-Hein
- Ali Emre Kavur
Wiesenfarth, M., Reinke, A., Landman, B.A., Eisenmann, M., Aguilera Saiz, L., Cardoso, M.J., Maier-Hein, L. and Kopp-Schneider, A. Methods and open-source toolkit for analyzing and visualizing challenge results. Sci Rep 11, 2369 (2021). https://doi.org/10.1038/s41598-021-82017-6
