Welcome to Dev Chakraborty's AI/FROC Research Dissemination Platform


This website is intended to provide an overview of the relevant repositories in my GitHub account (dpc10ster) and as a documentation and blogging platform for my research.


  • AI = Artificial Intelligence, formerly known as CAD (in the medical imaging context);
  • CAD = Computer Aided Detection;
  • FROC = Free-response Receiver Operating Characteristic, data collected in a visual search task;
  • ROC = Receiver Operating Characteristic, data collected in a binary classification task.

The primary repositories

  • This website describes five repositories addressing observer performance and artificial intelligence systems modeling, analysis and validation. Four of the repositories contain the source codes for online books.
  • The repositories are:
    • RJafroc is the R software package that provides the common thread on which the online books depend;
    • RJafrocQuickStart is an online book for those already somewhat familiar running Windows JAFROC. The obsolete Windows program has been replaced by RJafroc. This book dives into how to use RJafroc to analyze ROC or FROC datasets;
    • RJafrocRocBook is an online book providing background on the ROC paradigm, modeling and analysis;
    • RJafrocSigTestBook is an online book on significance testing of ROC or FROC datasets; significance testing refers to the procedure used to compute confidence intervals and p-values.
    • RJafrocFrocBook is an online book providing a detailed exposition of the FROC paradigm, modeling and analysis.


The first CRAN-posted version of RJafroc was used to support the R-code examples in the book: Chakraborty DP: Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples, Taylor-Francis LLC, 2017, and the online supplementary material.

Since its publication in 2017 RJafroc, on which the R code examples in the print book depend, has evolved considerably, causing many of the examples to “break” if one uses the most current version of RJafroc. The code will still run if one uses RJafroc 0.0.1 but this is sub-optimal as it misses out on many of the software improvements made since the print book appeared.

Unlike the print book (and the supplementary material) the online books described in the links contain embedded R code (using the bookdown R package). Accordingly the books and the software are automatically synchronized and any software inconsistencies will throw errors in GitHub Actions, see here for some examples.

This, and other considerations, led me to conclude that an update to the book was needed. To keep the book-length within a reasonable limit I decided to split the original book into four books: a quick start book for those needing to apply the methods with minimum theoretical fuss, a book focusing on ROC methodology, a book on the significance testing procedure and, my specialty, a book focusing on FROC methodology.

Documentation formats: HTML and PDF

  • RJafroc software:
    • HTML documentation (i.e., datasets, functions and update history, etc.) is available here. The software is fairly stable and undergoing occasional updates.
  • RJafrocQuickStart book:
    • The HTML online book is available here.
    • The PDF online book is available here (click on the Download link to get the pdf file).
  • RJafrocRocBook book:
    • The HTML online book is available here. The pdf file output has been removed in this book (4/8/23) as plots produced by plotly do not compile when using this format.
  • RJafrocSigTestBook book:
    • The HTML online book is available here.
    • The PDF online book is available here (click on the Download link to get the pdf file).
  • RJafrocFrocBook book:
    • The HTML online book is available here.
    • The PDF online book is available here (click on the Download link to get the pdf file).


The online books are under development and parts marked TBA (to be added) should be ignored. Each chapter has a How Much Finished (HMF) section giving a rough idea of the extent to which that chapter has been completed. I expect to complete the books in about three years (~Dec 2025).


While most of the applications in this package are geared toward analyzing radiologist performance in search tasks such as finding lesions in medical images, the software applies to any task involving detection and localization of targets in images. For example, the functions in RJafroc can be used to analyze the performance of artificial intelligence (AI) algorithms. Two applications to AI are here, specifically:

  • Measuring AI performance.
  • Optimizing the reporting threshold of an AI algorithm.

The radiological search model (RSM), described here is implemented in RJafroc. A fitting function RJafroc::FitRsmRoc estimates RSM parameters from ROC data These parameters are related to search and classification performances:

  • Search performance refers to finding lesions while simultaneously minimizing finding non-lesion locations
  • Classification performance measures ability to distinguish between lesion and non-lesion locations.

Knowing the individual performances allows principled optimization of reader or AI algorithm performance.

Relation to Windows software

  • RJafroc extends Windows JAFROC software and runs on multiple platforms.
  • Originally uploaded in 2004, the Windows software is many generations behind RJafroc software. The online book available here should allow one to quickly transition to RJafroc.
  • However, many users find the Windows JAFROC software both easy to use and useful. If you still need Windows JAFROC software it is still available here.

A note on the online distribution mechanism of the book {-}

  • In the hard-copy version of my 2017 book the online distribution mechanism was BitBucket.
  • At the time I used it BitBucket allowed code sharing within a closed group of a few users (e.g., myself and a grad student).
  • Since the purpose of open-source code is to encourage collaborations, this was, in hindsight, an unfortunate choice. Moreover, as my experience with R-packages grew, it became apparent that the vast majority of R-packages are shared on GitHub, not BitBucket.
  • For these reasons I have switched to GitHub. All previous instructions pertaining to BitBucket are obsolete.
  • In order to access GitHub material one needs to create a (free) GitHub account.
  • Go to this link and click on Sign Up.


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